{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"5class_classification_bidirectional_lstm.ipynb","provenance":[],"mount_file_id":"1kPvlu_PONbcH58X0sa_PQFebrS5cKrjE","authorship_tag":"ABX9TyMVsNL6y4Kjf6MUJy6qREt0"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"TsZv_FuqEpTw"},"source":["\n","# Import libraries and modules\n","import numpy as np\n","np.random.seed(123)  # for reproducibility\n","from sklearn.model_selection import StratifiedKFold\n","import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","%matplotlib notebook\n","import matplotlib.pyplot as plt\n","import keras\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import LSTM, GRU, Bidirectional\n","from keras.layers import Dropout\n","from keras.layers import Flatten, TimeDistributed, GlobalMaxPooling1D, GlobalAveragePooling1D\n","from keras.preprocessing import sequence\n","from keras.utils import np_utils\n","from keras import metrics\n","from keras import backend\n","from numpy import argmax\n","from numpy import asarray\n","from pylab import rcParams\n","from sklearn.metrics import confusion_matrix\n","rcParams['figure.figsize'] = 15, 10\n","\n","def rmse(y_true, y_pred):\n","    return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis = -1))\n","\n","def sensitivity(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     sensitivity = tp / (tp + fn)\n","     return sensitivity\n","\n","# Recall is the same as the sensitivity\n","def recall(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     recall = tp / (tp + fn)\n","     return recall\n","\n","def specificity(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     specificity = tn / (tn + fp)\n","     return specificity\n","\n","\n","def precision(y_true, y_pred):  \n","     y_pred_pos = backend.round(backend.clip(y_pred, 0, 1))\n","     y_pred_neg = 1 - y_pred_pos\n","     y_pos = backend.round(backend.clip(y_true, 0, 1))\n","     y_neg = 1 - y_pos\n","     tp = backend.sum(y_pos * y_pred_pos)\n","     tn = backend.sum(y_neg * y_pred_neg)\n","     fp = backend.sum(y_neg * y_pred_pos)\n","     fn = backend.sum(y_pos * y_pred_neg)\n","     precision = tp / (tp + fp)\n","     return precision\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":438},"id":"pJaLIuiBEwcs","executionInfo":{"status":"ok","timestamp":1619609838714,"user_tz":-330,"elapsed":6882,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"5acaa619-67cf-4f6f-9a10-513636614d06"},"source":["df=pd.read_csv(\"/content/drive/My Drive/mtech_finalyr_project/modified_Dataset/final_csv_with_shuffle.csv\")\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n"," 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<td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>495</th>\n","      <td>495</td>\n","      <td>213.0</td>\n","      <td>210.0</td>\n","      <td>210.0</td>\n","      <td>212.0</td>\n","      <td>194.0</td>\n","      <td>162.0</td>\n","      <td>121.0</td>\n","      <td>84.0</td>\n","      <td>45.0</td>\n","      <td>11.0</td>\n","      <td>-13.0</td>\n","      <td>-20.0</td>\n","      <td>-27.0</td>\n","      <td>-32.0</td>\n","      <td>-31.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-61.0</td>\n","      <td>-72.0</td>\n","      <td>-80.0</td>\n","      <td>-85.0</td>\n","      <td>-87.0</td>\n","      <td>-93.0</td>\n","      <td>-96.0</td>\n","      <td>-97.0</td>\n","      <td>-94.0</td>\n","      <td>-82.0</td>\n","      <td>-57.0</td>\n","      <td>-18.0</td>\n","      <td>-3.0</td>\n","      <td>-11.0</td>\n","      <td>-1.0</td>\n","      <td>41.0</td>\n","      <td>120.0</td>\n","      <td>181.0</td>\n","      <td>202.0</td>\n","      <td>198.0</td>\n","      <td>195.0</td>\n","      <td>197.0</td>\n","      <td>...</td>\n","      <td>-108.0</td>\n","      <td>-112.0</td>\n","      <td>-113.0</td>\n","      <td>-112.0</td>\n","      <td>-103.0</td>\n","      <td>-100.0</td>\n","      <td>-98.0</td>\n","      <td>-95.0</td>\n","      <td>-94.0</td>\n","      <td>-92.0</td>\n","      <td>-91.0</td>\n","      <td>-89.0</td>\n","      <td>-86.0</td>\n","      <td>-84.0</td>\n","      <td>-78.0</td>\n","      <td>-70.0</td>\n","      <td>-63.0</td>\n","      <td>-53.0</td>\n","      <td>-46.0</td>\n","      <td>-37.0</td>\n","      <td>-34.0</td>\n","      <td>-29.0</td>\n","      <td>-28.0</td>\n","      <td>-23.0</td>\n","      <td>-15.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-9.0</td>\n","      <td>-10.0</td>\n","      <td>-5.0</td>\n","      <td>-2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>-2.0</td>\n","      <td>-8.0</td>\n","      <td>-9.0</td>\n","      <td>-2.0</td>\n","      <td>20.0</td>\n","      <td>-231.0</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>496</th>\n","      <td>496</td>\n","      <td>24.0</td>\n","      <td>10.0</td>\n","      <td>-9.0</td>\n","      <td>-18.0</td>\n","      <td>-9.0</td>\n","      <td>-3.0</td>\n","      <td>-2.0</td>\n","      <td>-2.0</td>\n","      <td>-9.0</td>\n","      <td>-9.0</td>\n","      <td>-14.0</td>\n","      <td>-14.0</td>\n","      <td>-24.0</td>\n","      <td>-24.0</td>\n","      <td>-12.0</td>\n","      <td>-1.0</td>\n","      <td>13.0</td>\n","      <td>31.0</td>\n","      <td>60.0</td>\n","      <td>79.0</td>\n","      <td>98.0</td>\n","      <td>110.0</td>\n","      <td>104.0</td>\n","      <td>72.0</td>\n","      <td>40.0</td>\n","      <td>26.0</td>\n","      <td>24.0</td>\n","      <td>33.0</td>\n","      <td>40.0</td>\n","      <td>29.0</td>\n","      <td>13.0</td>\n","      <td>3.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>32.0</td>\n","      <td>52.0</td>\n","      <td>61.0</td>\n","      <td>54.0</td>\n","      <td>...</td>\n","      <td>55.0</td>\n","      <td>33.0</td>\n","      <td>15.0</td>\n","      <td>-5.0</td>\n","      <td>-18.0</td>\n","      <td>-24.0</td>\n","      <td>-28.0</td>\n","      <td>-15.0</td>\n","      <td>12.0</td>\n","      <td>33.0</td>\n","      <td>48.0</td>\n","      <td>47.0</td>\n","      <td>36.0</td>\n","      <td>19.0</td>\n","      <td>16.0</td>\n","      <td>31.0</td>\n","      <td>56.0</td>\n","      <td>69.0</td>\n","      <td>75.0</td>\n","      <td>67.0</td>\n","      <td>66.0</td>\n","      <td>76.0</td>\n","      <td>93.0</td>\n","      <td>88.0</td>\n","      <td>65.0</td>\n","      <td>56.0</td>\n","      <td>58.0</td>\n","      <td>63.0</td>\n","      <td>48.0</td>\n","      <td>29.0</td>\n","      <td>3.0</td>\n","      <td>-17.0</td>\n","      <td>-14.0</td>\n","      <td>-21.0</td>\n","      <td>-29.0</td>\n","      <td>-39.0</td>\n","      <td>-33.0</td>\n","      <td>-28.0</td>\n","      <td>-19.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>497</th>\n","      <td>497</td>\n","      <td>-20.0</td>\n","      <td>-12.0</td>\n","      <td>-6.0</td>\n","      <td>6.0</td>\n","      <td>8.0</td>\n","      <td>-2.0</td>\n","      <td>-22.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-49.0</td>\n","      <td>-46.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-46.0</td>\n","      <td>-46.0</td>\n","      <td>-35.0</td>\n","      <td>-23.0</td>\n","      <td>-13.0</td>\n","      <td>-14.0</td>\n","      <td>-17.0</td>\n","      <td>-23.0</td>\n","      <td>-25.0</td>\n","      <td>-18.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-16.0</td>\n","      <td>-20.0</td>\n","      <td>-36.0</td>\n","      <td>-46.0</td>\n","      <td>-54.0</td>\n","      <td>-64.0</td>\n","      <td>-64.0</td>\n","      <td>-63.0</td>\n","      <td>-55.0</td>\n","      <td>-53.0</td>\n","      <td>-44.0</td>\n","      <td>-43.0</td>\n","      <td>-47.0</td>\n","      <td>-44.0</td>\n","      <td>...</td>\n","      <td>26.0</td>\n","      <td>20.0</td>\n","      <td>21.0</td>\n","      <td>27.0</td>\n","      <td>33.0</td>\n","      <td>34.0</td>\n","      <td>42.0</td>\n","      <td>49.0</td>\n","      <td>37.0</td>\n","      <td>18.0</td>\n","      <td>-1.0</td>\n","      <td>-18.0</td>\n","      <td>-25.0</td>\n","      <td>-16.0</td>\n","      <td>6.0</td>\n","      <td>23.0</td>\n","      <td>37.0</td>\n","      <td>44.0</td>\n","      <td>39.0</td>\n","      <td>26.0</td>\n","      <td>14.0</td>\n","      <td>13.0</td>\n","      <td>11.0</td>\n","      <td>25.0</td>\n","      <td>38.0</td>\n","      <td>42.0</td>\n","      <td>31.0</td>\n","      <td>16.0</td>\n","      <td>8.0</td>\n","      <td>1.0</td>\n","      <td>7.0</td>\n","      <td>20.0</td>\n","      <td>19.0</td>\n","      <td>11.0</td>\n","      <td>10.0</td>\n","      <td>12.0</td>\n","      <td>9.0</td>\n","      <td>2.0</td>\n","      <td>-14.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>498</th>\n","      <td>498</td>\n","      <td>-104.0</td>\n","      <td>-100.0</td>\n","      <td>-94.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-87.0</td>\n","      <td>-88.0</td>\n","      <td>-90.0</td>\n","      <td>-83.0</td>\n","      <td>-75.0</td>\n","      <td>-69.0</td>\n","      <td>-59.0</td>\n","      <td>-50.0</td>\n","      <td>-38.0</td>\n","      <td>-42.0</td>\n","      <td>-50.0</td>\n","      <td>-67.0</td>\n","      <td>-84.0</td>\n","      <td>-92.0</td>\n","      <td>-96.0</td>\n","      <td>-97.0</td>\n","      <td>-99.0</td>\n","      <td>-100.0</td>\n","      <td>-96.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-93.0</td>\n","      <td>-107.0</td>\n","      <td>-126.0</td>\n","      <td>-131.0</td>\n","      <td>-136.0</td>\n","      <td>-128.0</td>\n","      <td>-121.0</td>\n","      <td>-107.0</td>\n","      <td>-95.0</td>\n","      <td>-87.0</td>\n","      <td>-82.0</td>\n","      <td>-82.0</td>\n","      <td>-86.0</td>\n","      <td>...</td>\n","      <td>-22.0</td>\n","      <td>-23.0</td>\n","      <td>-19.0</td>\n","      <td>-20.0</td>\n","      <td>-18.0</td>\n","      <td>-14.0</td>\n","      <td>-23.0</td>\n","      <td>-37.0</td>\n","      <td>-56.0</td>\n","      <td>-64.0</td>\n","      <td>-67.0</td>\n","      <td>-64.0</td>\n","      <td>-49.0</td>\n","      <td>-40.0</td>\n","      <td>-34.0</td>\n","      <td>-39.0</td>\n","      <td>-39.0</td>\n","      <td>-43.0</td>\n","      <td>-43.0</td>\n","      <td>-35.0</td>\n","      <td>-33.0</td>\n","      <td>-31.0</td>\n","      <td>-24.0</td>\n","      <td>-11.0</td>\n","      <td>1.0</td>\n","      <td>8.0</td>\n","      <td>6.0</td>\n","      <td>2.0</td>\n","      <td>-8.0</td>\n","      <td>-16.0</td>\n","      <td>-27.0</td>\n","      <td>-38.0</td>\n","      <td>-45.0</td>\n","      <td>-40.0</td>\n","      <td>-30.0</td>\n","      <td>-37.0</td>\n","      <td>-45.0</td>\n","      <td>-59.0</td>\n","      <td>-53.0</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>499</th>\n","      <td>499</td>\n","      <td>-43.0</td>\n","      <td>-57.0</td>\n","      <td>-86.0</td>\n","      <td>-106.0</td>\n","      <td>-106.0</td>\n","      <td>-120.0</td>\n","      <td>-111.0</td>\n","      <td>-88.0</td>\n","      <td>-81.0</td>\n","      <td>-90.0</td>\n","      <td>-109.0</td>\n","      <td>-102.0</td>\n","      <td>-69.0</td>\n","      <td>-20.0</td>\n","      <td>1.0</td>\n","      <td>25.0</td>\n","      <td>42.0</td>\n","      <td>68.0</td>\n","      <td>61.0</td>\n","      <td>36.0</td>\n","      <td>4.0</td>\n","      <td>4.0</td>\n","      <td>9.0</td>\n","      <td>5.0</td>\n","      <td>3.0</td>\n","      <td>-2.0</td>\n","      <td>-13.0</td>\n","      <td>-23.0</td>\n","      <td>-30.0</td>\n","      <td>-39.0</td>\n","      <td>-13.0</td>\n","      <td>29.0</td>\n","      <td>48.0</td>\n","      <td>63.0</td>\n","      <td>67.0</td>\n","      <td>90.0</td>\n","      <td>96.0</td>\n","      <td>84.0</td>\n","      <td>81.0</td>\n","      <td>...</td>\n","      <td>-117.0</td>\n","      <td>-130.0</td>\n","      <td>-132.0</td>\n","      <td>-134.0</td>\n","      <td>-150.0</td>\n","      <td>-161.0</td>\n","      <td>-191.0</td>\n","      <td>-180.0</td>\n","      <td>-145.0</td>\n","      <td>-135.0</td>\n","      <td>-123.0</td>\n","      <td>-139.0</td>\n","      <td>-127.0</td>\n","      <td>-103.0</td>\n","      <td>-111.0</td>\n","      <td>-95.0</td>\n","      <td>-82.0</td>\n","      <td>-55.0</td>\n","      <td>-19.0</td>\n","      <td>-11.0</td>\n","      <td>4.0</td>\n","      <td>16.0</td>\n","      <td>-22.0</td>\n","      <td>-1.0</td>\n","      <td>-20.0</td>\n","      <td>-11.0</td>\n","      <td>6.0</td>\n","      <td>17.0</td>\n","      <td>22.0</td>\n","      <td>3.0</td>\n","      <td>-9.0</td>\n","      <td>6.0</td>\n","      <td>39.0</td>\n","      <td>59.0</td>\n","      <td>80.0</td>\n","      <td>39.0</td>\n","      <td>21.0</td>\n","      <td>-8.0</td>\n","      <td>-43.0</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>500 rows × 4099 columns</p>\n","</div>"],"text/plain":["     Unnamed: 0      0      1      2      3  ...   4093   4094   4095   4096  tag\n","0             0  -18.0  -55.0 -126.0 -202.0  ... -224.0 -200.0 -127.0 -226.0    5\n","1             1  -26.0    1.0   29.0   41.0  ... -209.0 -207.0 -210.0 -107.0    5\n","2             2   68.0 -106.0 -149.0 -141.0  ...  460.0  343.0  247.0 -468.0    5\n","3             3  343.0  311.0  284.0  274.0  ...  527.0  480.0  397.0  217.0    5\n","4             4  -63.0 -107.0 -208.0 -310.0  ...   56.0   44.0  -37.0   28.0    5\n","..          ...    ...    ...    ...    ...  ...    ...    ...    ...    ...  ...\n","495         495  213.0  210.0  210.0  212.0  ...   -9.0   -2.0   20.0 -231.0    5\n","496         496   24.0   10.0   -9.0  -18.0  ...  -39.0  -33.0  -28.0  -19.0    1\n","497         497  -20.0  -12.0   -6.0    6.0  ...   12.0    9.0    2.0  -14.0    1\n","498         498 -104.0 -100.0  -94.0  -87.0  ...  -37.0  -45.0  -59.0  -53.0    3\n","499         499  -43.0  -57.0  -86.0 -106.0  ...   39.0   21.0   -8.0  -43.0    3\n","\n","[500 rows x 4099 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2sLqs0ufE5Xx","executionInfo":{"status":"ok","timestamp":1619609838717,"user_tz":-330,"elapsed":6869,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"e5d6ab70-5087-4061-9733-66d165c24781"},"source":["df.tag=df.tag.replace({1:0,2:1,3:2,5:4 , 4:3 })\n","df=df.drop(['Unnamed: 0'], axis = 1)\n","df=df.drop(['4096'], axis = 1)\n","df[\"tag\"].value_counts()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["4    100\n","3    100\n","2    100\n","1    100\n","0    100\n","Name: tag, dtype: int64"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4mvDoUmnFJte","executionInfo":{"status":"ok","timestamp":1619609838719,"user_tz":-330,"elapsed":6857,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"c53a0336-035a-4d7c-ff70-fb23183a24d2"},"source":["# Time Steps of LSTM\n","data_length = 4096\n","timesteps = 2048\n","data_dim = data_length//timesteps\n","data_dim\n","\n","\n","#breaking dataset into X nd y\n","df1=df.values     #df1 is numpy.ndarray and df is pandas.dataframe\n","X, y = df1[:, :-1], df1[:, -1]\n","print(df.shape)\n","print(\"shape of X\",X.shape)\n","print(\"shape of y\",y.shape)\n","\n","\n","#breaking X nd y into train nd test\n","from sklearn.model_selection import train_test_split\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1, stratify=y)\n","\n","print(\"\\nshape of X_train\",X_train.shape)\n","print(\"shape of X_test\",X_test.shape)\n","print(\"shape of y_train\",y_train.shape)\n","print(\"shape of y_test\",y_test.shape)\n","\n","X_train=X_train.reshape([X_train.shape[0], timesteps, data_dim])\n","X_test = X_test.reshape([X_test.shape[0], timesteps, data_dim])\n","y_train=np_utils.to_categorical(y_train, num_classes=5)\n","y_test=np_utils.to_categorical(y_test, num_classes=5)\n","\n","\n","\n","print(\"\\nshape of X_train\",X_train.shape)\n","print(\"shape of X_test\",X_test.shape)\n","print(\"shape of y_train\",y_train.shape)\n","print(\"shape of y_test\",y_test.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(500, 4097)\n","shape of X (500, 4096)\n","shape of y (500,)\n","\n","shape of X_train (350, 4096)\n","shape of X_test (150, 4096)\n","shape of y_train (350,)\n","shape of y_test (150,)\n","\n","shape of X_train (350, 2048, 2)\n","shape of X_test (150, 2048, 2)\n","shape of y_train (350, 5)\n","shape of y_test (150, 5)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"kvsLCRYPFd3N","executionInfo":{"status":"ok","timestamp":1619610217797,"user_tz":-330,"elapsed":385924,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"952883f1-49d6-4a7d-89f6-57d6464640a2"},"source":["nb_epoch=100\n","\n","model = Sequential()\n","\n","model.add(Bidirectional(LSTM(80, input_shape= (timesteps, data_dim), return_sequences = True)))\n","model.add(Dropout(0.1))\n","\n","model.add(TimeDistributed(Dense(50)))\n","\n","model.add(GlobalAveragePooling1D())\n","\n","model.add(Dense(5, activation='softmax'))\n","model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[sensitivity, specificity, 'accuracy'])\n","#print(model.summary())\n","history = model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=64, epochs=nb_epoch)\n","\n","\n","plt.plot(history.history['specificity'], 'b--')\n","plt.plot(history.history['sensitivity'], 'g--')\n","plt.plot(history.history['accuracy'], 'r--')\n","plt.title('Model Different Metrics')\n","plt.ylabel('Metrics')\n","plt.xlabel('Epoch #')\n","plt.show()\n","\n","\n","\n","scores = model.evaluate(X_test, y_test, verbose=0)\n","print(\"Sensitivity = %.2f%%\" % (scores[1]*100))\n","print(\"Specificity = %.2f%%\" % (scores[2]*100))\n","print(\"Classification Accuracy = %.2f%%\" % (scores[3]*100))\n","\n","\n","%matplotlib inline\n","plt.plot(history.history['accuracy'])\n","plt.plot(history.history['val_accuracy'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()\n","\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'val'], loc='upper left')\n","plt.grid()\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/100\n","6/6 [==============================] - 38s 823ms/step - loss: 1.5958 - sensitivity: 0.0000e+00 - specificity: 1.0000 - accuracy: 0.2421 - val_loss: 1.4112 - val_sensitivity: 0.0000e+00 - val_specificity: 1.0000 - val_accuracy: 0.5800\n","Epoch 2/100\n","6/6 [==============================] - 3s 580ms/step - loss: 1.4061 - sensitivity: 0.0000e+00 - specificity: 1.0000 - accuracy: 0.4925 - val_loss: 1.2900 - val_sensitivity: 0.0000e+00 - val_specificity: 1.0000 - val_accuracy: 0.5867\n","Epoch 3/100\n","6/6 [==============================] - 3s 580ms/step - loss: 1.2702 - sensitivity: 0.0250 - specificity: 1.0000 - accuracy: 0.6460 - val_loss: 1.1729 - val_sensitivity: 0.1136 - val_specificity: 0.9987 - val_accuracy: 0.6533\n","Epoch 4/100\n","6/6 [==============================] - 3s 577ms/step - loss: 1.1697 - sensitivity: 0.1369 - specificity: 1.0000 - accuracy: 0.6318 - val_loss: 1.0690 - val_sensitivity: 0.1449 - val_specificity: 0.9987 - val_accuracy: 0.6200\n","Epoch 5/100\n","6/6 [==============================] - 3s 576ms/step - loss: 1.0461 - sensitivity: 0.1555 - specificity: 1.0000 - accuracy: 0.6755 - val_loss: 0.9805 - val_sensitivity: 0.2069 - val_specificity: 0.9987 - val_accuracy: 0.6400\n","Epoch 6/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.9734 - sensitivity: 0.2114 - specificity: 0.9979 - accuracy: 0.6509 - val_loss: 0.9149 - val_sensitivity: 0.2533 - val_specificity: 0.9961 - val_accuracy: 0.6533\n","Epoch 7/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.9013 - sensitivity: 0.3064 - specificity: 0.9979 - accuracy: 0.6275 - val_loss: 0.8720 - val_sensitivity: 0.3670 - val_specificity: 0.9770 - val_accuracy: 0.6467\n","Epoch 8/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.8774 - sensitivity: 0.3553 - specificity: 0.9936 - accuracy: 0.6333 - val_loss: 0.8192 - val_sensitivity: 0.3660 - val_specificity: 0.9718 - val_accuracy: 0.6533\n","Epoch 9/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.8183 - sensitivity: 0.4088 - specificity: 0.9835 - accuracy: 0.6420 - val_loss: 0.7642 - val_sensitivity: 0.4176 - val_specificity: 0.9679 - val_accuracy: 0.6933\n","Epoch 10/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.7362 - sensitivity: 0.4718 - specificity: 0.9747 - accuracy: 0.6583 - val_loss: 0.7206 - val_sensitivity: 0.5365 - val_specificity: 0.9498 - val_accuracy: 0.6733\n","Epoch 11/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.6775 - sensitivity: 0.5585 - specificity: 0.9672 - accuracy: 0.7098 - val_loss: 0.7006 - val_sensitivity: 0.5975 - val_specificity: 0.9638 - val_accuracy: 0.7267\n","Epoch 12/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.6734 - sensitivity: 0.5734 - specificity: 0.9681 - accuracy: 0.7072 - val_loss: 0.6807 - val_sensitivity: 0.5881 - val_specificity: 0.9586 - val_accuracy: 0.7333\n","Epoch 13/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.6318 - sensitivity: 0.6428 - specificity: 0.9574 - accuracy: 0.7328 - val_loss: 0.6635 - val_sensitivity: 0.6184 - val_specificity: 0.9459 - val_accuracy: 0.7067\n","Epoch 14/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.6103 - sensitivity: 0.6307 - specificity: 0.9544 - accuracy: 0.7375 - val_loss: 0.6280 - val_sensitivity: 0.6643 - val_specificity: 0.9420 - val_accuracy: 0.7200\n","Epoch 15/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.5978 - sensitivity: 0.6514 - specificity: 0.9438 - accuracy: 0.7314 - val_loss: 0.6141 - val_sensitivity: 0.6643 - val_specificity: 0.9368 - val_accuracy: 0.7133\n","Epoch 16/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.5786 - sensitivity: 0.6658 - specificity: 0.9520 - accuracy: 0.7233 - val_loss: 0.6339 - val_sensitivity: 0.7003 - val_specificity: 0.9407 - val_accuracy: 0.7200\n","Epoch 17/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.5541 - sensitivity: 0.6483 - specificity: 0.9545 - accuracy: 0.7558 - val_loss: 0.6109 - val_sensitivity: 0.6700 - val_specificity: 0.9368 - val_accuracy: 0.6867\n","Epoch 18/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.5345 - sensitivity: 0.6807 - specificity: 0.9455 - accuracy: 0.7290 - val_loss: 0.6164 - val_sensitivity: 0.6955 - val_specificity: 0.9460 - val_accuracy: 0.7400\n","Epoch 19/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.5203 - sensitivity: 0.7017 - specificity: 0.9562 - accuracy: 0.7593 - val_loss: 0.5915 - val_sensitivity: 0.6700 - val_specificity: 0.9432 - val_accuracy: 0.7267\n","Epoch 20/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.4940 - sensitivity: 0.7042 - specificity: 0.9529 - accuracy: 0.7583 - val_loss: 0.6519 - val_sensitivity: 0.6847 - val_specificity: 0.9368 - val_accuracy: 0.7000\n","Epoch 21/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.5436 - sensitivity: 0.6624 - specificity: 0.9471 - accuracy: 0.7250 - val_loss: 0.6032 - val_sensitivity: 0.6804 - val_specificity: 0.9394 - val_accuracy: 0.7267\n","Epoch 22/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.5500 - sensitivity: 0.7230 - specificity: 0.9550 - accuracy: 0.7658 - val_loss: 0.6158 - val_sensitivity: 0.6695 - val_specificity: 0.9381 - val_accuracy: 0.7000\n","Epoch 23/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.4991 - sensitivity: 0.6881 - specificity: 0.9477 - accuracy: 0.7367 - val_loss: 0.5687 - val_sensitivity: 0.7164 - val_specificity: 0.9395 - val_accuracy: 0.7400\n","Epoch 24/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.4904 - sensitivity: 0.7470 - specificity: 0.9633 - accuracy: 0.7913 - val_loss: 0.5504 - val_sensitivity: 0.6856 - val_specificity: 0.9330 - val_accuracy: 0.7200\n","Epoch 25/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.4734 - sensitivity: 0.7446 - specificity: 0.9571 - accuracy: 0.7912 - val_loss: 0.5527 - val_sensitivity: 0.7273 - val_specificity: 0.9344 - val_accuracy: 0.7467\n","Epoch 26/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.4216 - sensitivity: 0.8066 - specificity: 0.9657 - accuracy: 0.8331 - val_loss: 0.5438 - val_sensitivity: 0.7211 - val_specificity: 0.9419 - val_accuracy: 0.7267\n","Epoch 27/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.3996 - sensitivity: 0.7784 - specificity: 0.9620 - accuracy: 0.8168 - val_loss: 0.5442 - val_sensitivity: 0.7420 - val_specificity: 0.9420 - val_accuracy: 0.7533\n","Epoch 28/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.4070 - sensitivity: 0.7874 - specificity: 0.9646 - accuracy: 0.8314 - val_loss: 0.5279 - val_sensitivity: 0.7012 - val_specificity: 0.9356 - val_accuracy: 0.7200\n","Epoch 29/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.4294 - sensitivity: 0.7701 - specificity: 0.9543 - accuracy: 0.7970 - val_loss: 0.6077 - val_sensitivity: 0.7211 - val_specificity: 0.9394 - val_accuracy: 0.7200\n","Epoch 30/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.3940 - sensitivity: 0.7660 - specificity: 0.9536 - accuracy: 0.7869 - val_loss: 0.5495 - val_sensitivity: 0.7017 - val_specificity: 0.9383 - val_accuracy: 0.7533\n","Epoch 31/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.4049 - sensitivity: 0.8160 - specificity: 0.9665 - accuracy: 0.8416 - val_loss: 0.5425 - val_sensitivity: 0.7420 - val_specificity: 0.9420 - val_accuracy: 0.7467\n","Epoch 32/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.4088 - sensitivity: 0.7819 - specificity: 0.9575 - accuracy: 0.8093 - val_loss: 0.5277 - val_sensitivity: 0.7524 - val_specificity: 0.9394 - val_accuracy: 0.7467\n","Epoch 33/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.4091 - sensitivity: 0.7760 - specificity: 0.9572 - accuracy: 0.7972 - val_loss: 0.5451 - val_sensitivity: 0.7164 - val_specificity: 0.9395 - val_accuracy: 0.7333\n","Epoch 34/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.4218 - sensitivity: 0.7959 - specificity: 0.9620 - accuracy: 0.8334 - val_loss: 0.6051 - val_sensitivity: 0.7008 - val_specificity: 0.9343 - val_accuracy: 0.7267\n","Epoch 35/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.5232 - sensitivity: 0.7419 - specificity: 0.9569 - accuracy: 0.7892 - val_loss: 0.6143 - val_sensitivity: 0.7064 - val_specificity: 0.9434 - val_accuracy: 0.7400\n","Epoch 36/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.4449 - sensitivity: 0.8015 - specificity: 0.9663 - accuracy: 0.8209 - val_loss: 0.6196 - val_sensitivity: 0.7211 - val_specificity: 0.9548 - val_accuracy: 0.7667\n","Epoch 37/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.4276 - sensitivity: 0.7762 - specificity: 0.9597 - accuracy: 0.8187 - val_loss: 0.5384 - val_sensitivity: 0.6960 - val_specificity: 0.9473 - val_accuracy: 0.7733\n","Epoch 38/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.3777 - sensitivity: 0.8245 - specificity: 0.9719 - accuracy: 0.8566 - val_loss: 0.5031 - val_sensitivity: 0.7524 - val_specificity: 0.9574 - val_accuracy: 0.7533\n","Epoch 39/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.4000 - sensitivity: 0.7844 - specificity: 0.9614 - accuracy: 0.8104 - val_loss: 0.5931 - val_sensitivity: 0.7173 - val_specificity: 0.9396 - val_accuracy: 0.7600\n","Epoch 40/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.3919 - sensitivity: 0.8281 - specificity: 0.9676 - accuracy: 0.8615 - val_loss: 0.5132 - val_sensitivity: 0.7420 - val_specificity: 0.9510 - val_accuracy: 0.7600\n","Epoch 41/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.3682 - sensitivity: 0.8204 - specificity: 0.9673 - accuracy: 0.8596 - val_loss: 0.5336 - val_sensitivity: 0.7884 - val_specificity: 0.9536 - val_accuracy: 0.7933\n","Epoch 42/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.3757 - sensitivity: 0.8093 - specificity: 0.9635 - accuracy: 0.8328 - val_loss: 0.5727 - val_sensitivity: 0.7273 - val_specificity: 0.9370 - val_accuracy: 0.7467\n","Epoch 43/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.3474 - sensitivity: 0.8222 - specificity: 0.9665 - accuracy: 0.8464 - val_loss: 0.5432 - val_sensitivity: 0.7424 - val_specificity: 0.9395 - val_accuracy: 0.7533\n","Epoch 44/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.3649 - sensitivity: 0.8279 - specificity: 0.9658 - accuracy: 0.8468 - val_loss: 0.5280 - val_sensitivity: 0.7216 - val_specificity: 0.9408 - val_accuracy: 0.7400\n","Epoch 45/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.3214 - sensitivity: 0.8210 - specificity: 0.9627 - accuracy: 0.8336 - val_loss: 0.5635 - val_sensitivity: 0.7424 - val_specificity: 0.9434 - val_accuracy: 0.7600\n","Epoch 46/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.3252 - sensitivity: 0.8227 - specificity: 0.9646 - accuracy: 0.8384 - val_loss: 0.5530 - val_sensitivity: 0.7424 - val_specificity: 0.9421 - val_accuracy: 0.7600\n","Epoch 47/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.3061 - sensitivity: 0.8607 - specificity: 0.9726 - accuracy: 0.8770 - val_loss: 0.5673 - val_sensitivity: 0.7325 - val_specificity: 0.9421 - val_accuracy: 0.7600\n","Epoch 48/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.3097 - sensitivity: 0.8445 - specificity: 0.9681 - accuracy: 0.8597 - val_loss: 0.5637 - val_sensitivity: 0.7528 - val_specificity: 0.9485 - val_accuracy: 0.7733\n","Epoch 49/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.3125 - sensitivity: 0.8498 - specificity: 0.9708 - accuracy: 0.8718 - val_loss: 0.9998 - val_sensitivity: 0.6856 - val_specificity: 0.9240 - val_accuracy: 0.6933\n","Epoch 50/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.9006 - sensitivity: 0.6998 - specificity: 0.9282 - accuracy: 0.7058 - val_loss: 0.5867 - val_sensitivity: 0.6652 - val_specificity: 0.9485 - val_accuracy: 0.7333\n","Epoch 51/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.5855 - sensitivity: 0.6844 - specificity: 0.9397 - accuracy: 0.7235 - val_loss: 0.6512 - val_sensitivity: 0.6288 - val_specificity: 0.9305 - val_accuracy: 0.6667\n","Epoch 52/100\n","6/6 [==============================] - 3s 585ms/step - loss: 0.5075 - sensitivity: 0.6866 - specificity: 0.9522 - accuracy: 0.7376 - val_loss: 0.5994 - val_sensitivity: 0.7268 - val_specificity: 0.9433 - val_accuracy: 0.7333\n","Epoch 53/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.4079 - sensitivity: 0.7616 - specificity: 0.9505 - accuracy: 0.7805 - val_loss: 0.5954 - val_sensitivity: 0.7467 - val_specificity: 0.9458 - val_accuracy: 0.7400\n","Epoch 54/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.4047 - sensitivity: 0.7585 - specificity: 0.9544 - accuracy: 0.7952 - val_loss: 0.4852 - val_sensitivity: 0.7784 - val_specificity: 0.9561 - val_accuracy: 0.7867\n","Epoch 55/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.3503 - sensitivity: 0.8284 - specificity: 0.9658 - accuracy: 0.8448 - val_loss: 0.5117 - val_sensitivity: 0.7623 - val_specificity: 0.9496 - val_accuracy: 0.7600\n","Epoch 56/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.3545 - sensitivity: 0.8152 - specificity: 0.9661 - accuracy: 0.8352 - val_loss: 0.5146 - val_sensitivity: 0.7831 - val_specificity: 0.9471 - val_accuracy: 0.7800\n","Epoch 57/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.3652 - sensitivity: 0.8107 - specificity: 0.9666 - accuracy: 0.8388 - val_loss: 0.6433 - val_sensitivity: 0.7211 - val_specificity: 0.9394 - val_accuracy: 0.7400\n","Epoch 58/100\n","6/6 [==============================] - 3s 584ms/step - loss: 0.3678 - sensitivity: 0.8130 - specificity: 0.9635 - accuracy: 0.8469 - val_loss: 0.5119 - val_sensitivity: 0.6913 - val_specificity: 0.9267 - val_accuracy: 0.7133\n","Epoch 59/100\n","6/6 [==============================] - 3s 584ms/step - loss: 0.3560 - sensitivity: 0.8421 - specificity: 0.9668 - accuracy: 0.8564 - val_loss: 0.6454 - val_sensitivity: 0.7263 - val_specificity: 0.9419 - val_accuracy: 0.7400\n","Epoch 60/100\n","6/6 [==============================] - 3s 583ms/step - loss: 0.3986 - sensitivity: 0.7942 - specificity: 0.9566 - accuracy: 0.8118 - val_loss: 0.5689 - val_sensitivity: 0.7315 - val_specificity: 0.9381 - val_accuracy: 0.7400\n","Epoch 61/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.3686 - sensitivity: 0.8342 - specificity: 0.9649 - accuracy: 0.8486 - val_loss: 0.5764 - val_sensitivity: 0.7367 - val_specificity: 0.9471 - val_accuracy: 0.7400\n","Epoch 62/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.3436 - sensitivity: 0.8326 - specificity: 0.9674 - accuracy: 0.8531 - val_loss: 0.5337 - val_sensitivity: 0.7519 - val_specificity: 0.9483 - val_accuracy: 0.7533\n","Epoch 63/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.3230 - sensitivity: 0.8666 - specificity: 0.9697 - accuracy: 0.8713 - val_loss: 0.5485 - val_sensitivity: 0.7467 - val_specificity: 0.9471 - val_accuracy: 0.7533\n","Epoch 64/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.3221 - sensitivity: 0.8741 - specificity: 0.9717 - accuracy: 0.8723 - val_loss: 0.5227 - val_sensitivity: 0.7879 - val_specificity: 0.9560 - val_accuracy: 0.7800\n","Epoch 65/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.2946 - sensitivity: 0.8467 - specificity: 0.9645 - accuracy: 0.8550 - val_loss: 0.5239 - val_sensitivity: 0.7931 - val_specificity: 0.9561 - val_accuracy: 0.7933\n","Epoch 66/100\n","6/6 [==============================] - 3s 583ms/step - loss: 0.3009 - sensitivity: 0.8657 - specificity: 0.9709 - accuracy: 0.8780 - val_loss: 0.4853 - val_sensitivity: 0.7727 - val_specificity: 0.9471 - val_accuracy: 0.7800\n","Epoch 67/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.2644 - sensitivity: 0.8927 - specificity: 0.9779 - accuracy: 0.9069 - val_loss: 0.5611 - val_sensitivity: 0.7675 - val_specificity: 0.9458 - val_accuracy: 0.7600\n","Epoch 68/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.2775 - sensitivity: 0.8646 - specificity: 0.9714 - accuracy: 0.8742 - val_loss: 0.5068 - val_sensitivity: 0.7936 - val_specificity: 0.9523 - val_accuracy: 0.7933\n","Epoch 69/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.2752 - sensitivity: 0.8752 - specificity: 0.9733 - accuracy: 0.8828 - val_loss: 0.4822 - val_sensitivity: 0.7727 - val_specificity: 0.9458 - val_accuracy: 0.7600\n","Epoch 70/100\n","6/6 [==============================] - 3s 583ms/step - loss: 0.2382 - sensitivity: 0.8995 - specificity: 0.9780 - accuracy: 0.9062 - val_loss: 0.4790 - val_sensitivity: 0.7879 - val_specificity: 0.9509 - val_accuracy: 0.7733\n","Epoch 71/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.2477 - sensitivity: 0.8895 - specificity: 0.9744 - accuracy: 0.8985 - val_loss: 0.5771 - val_sensitivity: 0.7727 - val_specificity: 0.9484 - val_accuracy: 0.7733\n","Epoch 72/100\n","6/6 [==============================] - 3s 578ms/step - loss: 0.2757 - sensitivity: 0.8775 - specificity: 0.9731 - accuracy: 0.8863 - val_loss: 0.4898 - val_sensitivity: 0.7670 - val_specificity: 0.9444 - val_accuracy: 0.7400\n","Epoch 73/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.2489 - sensitivity: 0.8675 - specificity: 0.9705 - accuracy: 0.8760 - val_loss: 0.5207 - val_sensitivity: 0.7983 - val_specificity: 0.9561 - val_accuracy: 0.8000\n","Epoch 74/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.2670 - sensitivity: 0.8660 - specificity: 0.9694 - accuracy: 0.8729 - val_loss: 0.4757 - val_sensitivity: 0.7775 - val_specificity: 0.9483 - val_accuracy: 0.7667\n","Epoch 75/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.2449 - sensitivity: 0.8703 - specificity: 0.9719 - accuracy: 0.8831 - val_loss: 0.6028 - val_sensitivity: 0.7983 - val_specificity: 0.9535 - val_accuracy: 0.7933\n","Epoch 76/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.2767 - sensitivity: 0.8813 - specificity: 0.9720 - accuracy: 0.8802 - val_loss: 0.5784 - val_sensitivity: 0.8035 - val_specificity: 0.9548 - val_accuracy: 0.7933\n","Epoch 77/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.2270 - sensitivity: 0.9236 - specificity: 0.9830 - accuracy: 0.9258 - val_loss: 0.5481 - val_sensitivity: 0.8035 - val_specificity: 0.9522 - val_accuracy: 0.7933\n","Epoch 78/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.2136 - sensitivity: 0.9223 - specificity: 0.9824 - accuracy: 0.9237 - val_loss: 0.5338 - val_sensitivity: 0.7931 - val_specificity: 0.9522 - val_accuracy: 0.7800\n","Epoch 79/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.2465 - sensitivity: 0.9048 - specificity: 0.9798 - accuracy: 0.9150 - val_loss: 0.5559 - val_sensitivity: 0.8239 - val_specificity: 0.9560 - val_accuracy: 0.8000\n","Epoch 80/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.3067 - sensitivity: 0.8518 - specificity: 0.9714 - accuracy: 0.8723 - val_loss: 0.5666 - val_sensitivity: 0.7983 - val_specificity: 0.9535 - val_accuracy: 0.7933\n","Epoch 81/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.2467 - sensitivity: 0.9081 - specificity: 0.9782 - accuracy: 0.9128 - val_loss: 0.5263 - val_sensitivity: 0.7775 - val_specificity: 0.9496 - val_accuracy: 0.7533\n","Epoch 82/100\n","6/6 [==============================] - 3s 583ms/step - loss: 0.2538 - sensitivity: 0.8789 - specificity: 0.9724 - accuracy: 0.8824 - val_loss: 0.4919 - val_sensitivity: 0.7618 - val_specificity: 0.9431 - val_accuracy: 0.7333\n","Epoch 83/100\n","6/6 [==============================] - 3s 576ms/step - loss: 0.2451 - sensitivity: 0.8806 - specificity: 0.9729 - accuracy: 0.8803 - val_loss: 0.5535 - val_sensitivity: 0.7779 - val_specificity: 0.9497 - val_accuracy: 0.7733\n","Epoch 84/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.2117 - sensitivity: 0.9076 - specificity: 0.9776 - accuracy: 0.9081 - val_loss: 0.5106 - val_sensitivity: 0.7779 - val_specificity: 0.9471 - val_accuracy: 0.7733\n","Epoch 85/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.2031 - sensitivity: 0.9109 - specificity: 0.9793 - accuracy: 0.9198 - val_loss: 0.5945 - val_sensitivity: 0.7831 - val_specificity: 0.9497 - val_accuracy: 0.7800\n","Epoch 86/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.2078 - sensitivity: 0.9046 - specificity: 0.9784 - accuracy: 0.9079 - val_loss: 0.5685 - val_sensitivity: 0.7367 - val_specificity: 0.9368 - val_accuracy: 0.7267\n","Epoch 87/100\n","6/6 [==============================] - 3s 584ms/step - loss: 0.2576 - sensitivity: 0.8531 - specificity: 0.9664 - accuracy: 0.8640 - val_loss: 0.5894 - val_sensitivity: 0.7926 - val_specificity: 0.9521 - val_accuracy: 0.7667\n","Epoch 88/100\n","6/6 [==============================] - 3s 582ms/step - loss: 0.1993 - sensitivity: 0.9200 - specificity: 0.9806 - accuracy: 0.9238 - val_loss: 0.6182 - val_sensitivity: 0.7884 - val_specificity: 0.9497 - val_accuracy: 0.7867\n","Epoch 89/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.1965 - sensitivity: 0.9147 - specificity: 0.9822 - accuracy: 0.9285 - val_loss: 0.5499 - val_sensitivity: 0.7363 - val_specificity: 0.9393 - val_accuracy: 0.7267\n","Epoch 90/100\n","6/6 [==============================] - 3s 577ms/step - loss: 0.2147 - sensitivity: 0.9143 - specificity: 0.9809 - accuracy: 0.9230 - val_loss: 0.5537 - val_sensitivity: 0.7779 - val_specificity: 0.9471 - val_accuracy: 0.7667\n","Epoch 91/100\n","6/6 [==============================] - 3s 583ms/step - loss: 0.1840 - sensitivity: 0.9247 - specificity: 0.9825 - accuracy: 0.9306 - val_loss: 0.5372 - val_sensitivity: 0.7775 - val_specificity: 0.9470 - val_accuracy: 0.7600\n","Epoch 92/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.1861 - sensitivity: 0.9141 - specificity: 0.9794 - accuracy: 0.9215 - val_loss: 0.5158 - val_sensitivity: 0.7580 - val_specificity: 0.9434 - val_accuracy: 0.7733\n","Epoch 93/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.2243 - sensitivity: 0.9008 - specificity: 0.9770 - accuracy: 0.9030 - val_loss: 0.5511 - val_sensitivity: 0.7670 - val_specificity: 0.9431 - val_accuracy: 0.7400\n","Epoch 94/100\n","6/6 [==============================] - 3s 584ms/step - loss: 0.2708 - sensitivity: 0.8555 - specificity: 0.9675 - accuracy: 0.8689 - val_loss: 0.5378 - val_sensitivity: 0.7723 - val_specificity: 0.9457 - val_accuracy: 0.7467\n","Epoch 95/100\n","6/6 [==============================] - 3s 583ms/step - loss: 0.2383 - sensitivity: 0.8720 - specificity: 0.9698 - accuracy: 0.8727 - val_loss: 0.6101 - val_sensitivity: 0.7775 - val_specificity: 0.9457 - val_accuracy: 0.7533\n","Epoch 96/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.2573 - sensitivity: 0.8866 - specificity: 0.9725 - accuracy: 0.8851 - val_loss: 0.4676 - val_sensitivity: 0.8030 - val_specificity: 0.9534 - val_accuracy: 0.7800\n","Epoch 97/100\n","6/6 [==============================] - 3s 580ms/step - loss: 0.2610 - sensitivity: 0.8624 - specificity: 0.9687 - accuracy: 0.8646 - val_loss: 0.5679 - val_sensitivity: 0.7628 - val_specificity: 0.9433 - val_accuracy: 0.7667\n","Epoch 98/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.2504 - sensitivity: 0.8644 - specificity: 0.9691 - accuracy: 0.8726 - val_loss: 0.5668 - val_sensitivity: 0.7936 - val_specificity: 0.9510 - val_accuracy: 0.7867\n","Epoch 99/100\n","6/6 [==============================] - 3s 579ms/step - loss: 0.2228 - sensitivity: 0.9131 - specificity: 0.9821 - accuracy: 0.9211 - val_loss: 0.5991 - val_sensitivity: 0.7879 - val_specificity: 0.9483 - val_accuracy: 0.7733\n","Epoch 100/100\n","6/6 [==============================] - 3s 581ms/step - loss: 0.2148 - sensitivity: 0.8992 - specificity: 0.9788 - accuracy: 0.9067 - val_loss: 0.5793 - val_sensitivity: 0.7779 - val_specificity: 0.9445 - val_accuracy: 0.7667\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Sensitivity = 76.99%\n","Specificity = 94.25%\n","Classification Accuracy = 76.67%\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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oVa6yOul4uBFt6ypVLiupvfGSn0btmYNWkiBIIg+B+nS47gDmDWKb9rRPzxqSZ6tYgk41gh+7MKTrkZgiAIviTA1wYopUZghGBYFceMBcYCxMXFkZycXKd75eTknHBur6AWWLcuoti5A4Cvf/6DfnE+f1s8SkXtbuj4Y5vBP9vtj20GD7dba+21H6ANsL6K/T2B7UCnml6zX79+uq7MmzfvxI2zntL6xTidX1CoOzwzQ786a2Odr3+6UmG7Gzj+2Gat/bPd/thmrWvfbmC5ruS56rPQkFKqFfAdcJPWeouv7HBPNRGcvZsuzRpJwlgQBL/DazEQpdQkIAmIUUqlAc8DNgCt9TjgOSAa+EApBeDQWvf3lj2VEu9KGO9fS68Wnfl+1V6cTo3Fok65KYIgCL7Aa0Kgtb6umv13And66/41pmkXsAZC+ip6tRzEl4t3s+NQDh1iI3xtmSAIwinhdKka8h0BgWY8QfpqereMBGDVHgkPCYLgP4gQACT0gfTVtIsOJSI4QGYiFQTBrxAhAEjoC/ZsLIe307tlY1buPlL9OYIgCA0EEQIwHgFA+kr6tmrCloxsWbpSEAS/QYQAoGlnsIVC+ir6tm6CU8NaCQ8JguAniBAAWKzQrBfsXUnvFmZK6pV7JDwkCIJ/IELgJqEv7F9LZJCiQ2y4VA4JguA3iBC4SegDjgI4uJE+LRuzKvWoexoMQRCEBo0IgZvmfc1vV57gcK6dXZl5vrVJEAThFCBC4KZJWwiKNELQqgkAqyRPIAiCHyBC4MZigQSTMO4QG054UIAkjAVB8AtECEqT0BcyUrA67fRu2VgSxoIg+AUiBKVJ6APOIshYT59Wjdm0P5s8uwwsEwShYSNCUJrm/czvtBX0bdWEYqdmTWqWb20SBEHwMiIEpYlsAY2aw56F9GllBpYt33XYx0YJgiB4FxGC0igFrc+A3YtoHGKjZ4tIkrcc9LVVgiAIXkWEoDythkDOfji8g6TOsazac4QjuXZfWyUIguA1RAjK03qo+b1nESM6N8WpYf5W8QoEQWi4iBCUp2lnCI2G3Qvp2aIxUWGBJG8WIRAEoeEiQlAepUx4aPefWC2Kszo15fctByl2yrxDgiA0TLwmBEqpCUqpA0qp9ZXsV0qpd5RS25RSa5VSfb1lS61pfQYc2QXH0hmRGMvhXDtr0mRwmSAIDRNvegSfASOr2H8h0NH1Mxb4rxdtqR2thpjfuxdyZscYLAqSNx3wrU2CIAhewmtCoLWeD1RVhD8a+EIbFgONlVLNvGVPrYjvCYHhsGcRjUMD6duqCfMkTyCUJucg5B7ytRWC4BECfHjv5kBqqddprm37yh+olBqL8RqIi4sjOTm5TjfMycmp8bk9wzoSuOFXlocl0zrQzre7i/hh9lwaB9W/tEpt2t1Q8Habe6z9B1oFsL7H37x2j7ogn7X/4Ml2+1IIaozWejwwHqB///46KSmpTtdJTk6mxudaLoK5L5E0sCdNO1n5dusf2KM7ktS/ZZ3u7Utq1e4GgtfbvPFvYNWn3fsqn7X/4Ml2+7J7uxco/VRt4dp2enB8PMFiujZrRFyjIJI3S55AcFGYDYU5vrZCEDyCL4VgGnCzq3poMJCltT4hLOQzEvqCLRS2zUEpxYjOsSzYcoiiYqevLRNOB+y5YBchEBoG3iwfnQQsAjorpdKUUncope5WSt3tOmQmsAPYBnwE3OMtW+qELRg6nAubpoPTyYjEWLILHSyTSegEMCJQmO1rKwTBI3gtR6C1vq6a/Rq411v39whdR8PGaZC6hGEdBhBotTBv0wHOaB/ja8sEX1LsAEeB+dvpNKvbCUI9Rr7BVdHxfLAGwcZphAUFMKhdFHNlPIFQOiRUlOs7OwTBQ4gQVEVwI2h/Nmz8CbRmROdYth/MZU9mnq8tE3yJvdTDXxLGQgNAhKA6ul4KWamQvpIRibEAzN2U4WOjBJ9S2iOQhLHQABAhqI7OF4IlADb8SNuYMNrGhDFXRhn7N6Uf/oXHfGeHIHgIEYLqCGkCbc+EDdOOh4cW78iURe39mdLhIAkNCQ0AEYKa0OVSOLITMtZzdmIsdoeThdsyfW2V4CtK5wgkNCQ0AGokBEqpB5VSjVyDvz5RSq1USp3vbeNOGxIvBmWBdd8wsG0UYYFWft0geQK/xS4egdCwqKlHcLvW+hhwPtAEuAl41WtWnW6EN4Uul8CyCQTajzKyezNmrNsn4SF/pUyyWAaVCfWfmgqBcv0eBXyptU4ptc0/OOtJ80+/+APGDGhJTqGDmev2+9oqwReUyRGIEAj1n5oKwQql1C8YIZitlIoA/GvSnbhuJlew5EMGxCnaxoQxZXlq9ecJDQ93jkBZJDQkNAhqKgR3AE8BA7TWeYANuM1rVp2unPUkFB5DLfkvV/dvwdKdh9l5SEaW+h32HLCFmcWLJFksNABqKgRDgM1a66NKqRuBZ4Es75l1mhLf3SSOF4/jqq4RWBR8I16B/2HPgUCXEIhHIDQAaioE/wXylFK9gEeB7cAXXrPqdOasJ6Ewi9gNnzGicyxTV6ThkKmp/YvCHAgKh6AISRYLDYKaCoHDNVvoaOA9rfX7QIT3zDqNadYTOpwHKz7lmr7NOJBdyO9bZKSxX2HPNR5BULgki4UGQU2FIFsp9TSmbHSGUsqCyRP4J/1vg+x9nGNdRUx4IP9bJuEhv8KeA4EREhoSGgw1FYIxQCFmPMF+zLKS/+c1q053Ol4AEQkErPyUy/s0Z+6mAxzKKfS1VcKpwp0jCIqQZLHQIKiRELge/l8DkUqpi4ECrbV/5ggArAHQ92bYPpfrOmkcTs0Pq06f5ZYFL2PPNWEh8QiEBkJNp5i4BlgKXA1cAyxRSl3lTcNOe/reDErRbvdUerdszJTlqZg0itDgKSztEUiOQKj/1DQ09DfMGIJbtNY3AwOBv3vPrHpAZHMTIlr1FWP6xrMlI4e1af5XUeuX2HNNjiDI5RFIB0Co59RUCCxa69JrNGbW4tyGS//bIfcAo0NWE2yzyEhjf0Br4wW4xxE4i8Ah+SGhflPTh/nPSqnZSqlblVK3AjOAmdWdpJQaqZTarJTappR6qoL9rZRS85RSq5RSa5VSo2pnvo/pcA5EtiR07ReM6t6MaavTybcX+9oqwZs4CkA7S8YRgCSMhXpPTZPFjwPjgZ6un/Fa6yerOkcpZQXeBy4EugLXKaW6ljvsWWCK1roPcC3wQe3M9zEWK/S5EXYkc2OiIrvQwewUmYiuQeNODge6ksUgYwmEek+Nwzta62+11o+4fr6vwSkDgW1a6x1aazswGTMgrcxlgUauvyOB9Jrac9rQ+wZA0SdzBq2iQmVMQUPHXkoIxCMQGggBVe1USmVjHtYn7AK01rpRBfvcNAdKPxXTgEHljnkB+EUpdT8QBpxbiR1jgbEAcXFxJCcnV2V2peTk5NT53Kro2aQXoUsm0D+mD99tz+ObmXNpGnr6pFC81e7TGW+1OSxnJwOA9Vt3UWwNoRfw3S+/sz0wgwHxVf47nRLks/YfPNnuKr+5WmtvTyNxHfCZ1voNpdQQ4EulVHetdZnJe7TW4zGhKfr376+TkpLqdLPk5GTqem6VxDwIU2/jmYHw/Q5Is7Xg6qROnr9PHfFau09jvNbmPcGwHLr3HWQqh9bC7hzN9Fwrj1/rhfvVEvms/QdPttub3da9QMtSr1u4tpXmDmAKgNZ6ERAMxHjRJu+QeBGERBGz5X8MbR/D1BVpOJ1SUtggKZ0jCHLlCOy57M8qkHEkQr3Fm0KwDOiolGqrlArEJIOnlTtmD3AOgFKqC0YI6t8MbgFB0HMMbJrB9T3D2Xs0n8U7ZHH7BsnxHEHY8WSxtSiHQoeTo3lFPjRMEOqO14RAa+0A7gNmAxsx1UEpSql/KqUudR32KHCXUmoNMAm4VdfXblXfm6DYznlFyUQEB/DNijRfWyR4gwqSxQFFZnGi/ccKfGWVIJwUXs1oaq1naq07aa3ba61fdm17Tms9zfX3Bq31UK11L611b631L960x6vEdYPm/bCtnMDonnHMWr+PYwXSQ2xwuJepLFU+GlDsEoIsEQKhfnL6lLY0BM54ADK3cWfUWgqKnExfs8/XFgmexj1mICjcTD4YEEKgQzwCoX4jQuBJulwKTRNpnfIBnZqGMnnZHkkgNjTsuWAJAGsgADoonEBnHgD7xCMQ6ikiBJ7EYoHhj6EObuTZDjtZm5bF/K2HfG2V4EnsOSYkpBQAOjCcMIwAZIgQCPUUEQJP0/0KiGrPsPQJNI8M5j+/bhGvoCFhzy2ZWgJw2sIJIx+AfRIaEuopIgSexmKF4Y9iyVjHK933sjr1KMmypnHDoTC7ZPwA4AgII1yJRyDUb0QIvEHPa6BxK87c9S7dIgt5a85W8QoaCu6F6104AsIIJ5+Y8CD2ZeX70DBBqDsiBN7AaoNL30VlpTIp4B8cSN1O8mbxChoE7hyB+6U1lDDyadc0jGMFDvLsDh8aJwh1Q4TAW7RLgpu+I8KRyfch/+R/s+eJV9AQKJcjsFtDCVcFtG9qvAQZSyDUR0QIvEnrM1C3TKexzcGLhx9n2ZL5vrZIOFnK5QgKLKGEUUC7GLNNxhII9RERAm+T0BvL7bNwqgC6zr4evWexry0SToZyOYJ8FUqoKqR9TDAgHoFQPxEhOAUExndhUdLXHCgOx/n5aNg6x9cmCXWlXI4gT4UA0D7SjCsQj0Coj4gQnCJGDRvE/cGvsFsloCeNgaUfmYXQhfpDscOsWVxKCHK1EYKYQDuNggPEIxDqJSIEp4jAAAvXnt2f0TnPcCR+OMx8DH56EBx2X5sm1BT3zKOlcgQ5mJBQsDOPZpEhIgRCvUSE4BRyTf8WhDWK4m7Ho+hhj8DKz+GL0eAo9LVpQk04PvNoSY4gWxshsBblEhcZLKEhoV4iQnAKCQqw8uC5HVm65xifBt8Ml/0X9iyERe979kZaS9jJG5Rei8BFVrERAgqzadYoWCaeE+olIgSnmGsHtOTcLrG8OmsT65teBIkXw/z/gywPLWTjLIb3B8Fv//TM9YQSKhICZ9DxfXGRwVyZPxXnl1d4V4gdhSL0gkcRITjFKKV4/apeRIUFcv+kVeSNeNH8U89+xjM32L0QDm2GpeOhIMsz1xQMhSfmCI4Wu4SgMJtmjQK52Toby/bfzOdwUvfKhknXw+EdZbc77PBmV1j28cldXxBKIULgA6LCAnnr2t7szszl779nw5mPwoYfYfvcEw8uKoC1U0ri09Wx7huw2EzvddVXnjXc36kgR3DUYdYloDCHTkWbSVCHzevln5zcvVKXwuYZsGV22e2Ht0PeIUj54eSuLwilECHwEYPbRXPviA58uzKN5c1vhKh2MPNxyM4oOajgGHx9FXx3F0y+ofqksqMQNvxgpsJuNQSWfGhCRYJnOB4aiji+KbPIJQT2bNoc+BW7tpLa6nLYMA1yDtT9Xgc3u35vqnh76uKS1dIE4SQRIfAh9yR1oGlEEP/+bRdc/B84mgrvD4TVEyH3EHxxqQkx9L0ZdswzglDVg33bHBMO6nENDP4rHN0Nm2edsvZ4jIJjZtCd+6F3unBcCEp5BHYrxVihMJvGu2axwNmTJc1vBmcRrPqy7vdyC8DBLWW3H3K9djpg54K6X1+omrzDUOQ/s8l6VQiUUiOVUpuVUtuUUk9Vcsw1SqkNSqkUpdREb9pzuhESaOWepPYs3nGYhc7u8Nc/IbYL/PBXeLsXHNgI106ES9+FC14x4aNpD8DyT2Hq7fBWD1hZ6mGz7hsIjYF2Z0HniyCyFSz+r9l3ZBd8e6dJTJ+OZO+H5Fdh/Ah4rTV8fSV8c6uvrSpLBTmC3KJiCiyhsHM+1mNp/MJgNjvioe2ZsPyzuntkx4WgAo8gohnYwozwC54ne78puPjpIV9bcsrwmhAopazA+8CFQFfgOqVU13LHdASeBoZqrbsB/vPOu7huYCuaRQbzxq9b0NEd4NaZMOrf0KQt3PgddB5pDhxyLwx/DFZ/BdMfMp5CQIgZlLZ9rulFb54F3S4302BbA2DQWNj9B502f2C+2Ou+geTXzBfdU+QfNSGtvMN1Oz9jgxGo/3QzQhAQZNrZ92Y4sMGztp4s7hyBrcQjyCssxm4Nhb0rwGIjJWIo+48VQv87IGtP3R7WWhsBsAZC/mHjHbo5tAXiuhmh2f7bSTZIOAFnsfG8cw/AphmeHeOzeyH8uzPknH5T0nvTIxgIbNNa7yFkY2oAACAASURBVNBa24HJwOhyx9wFvK+1PgKgtT6JoGr9JNhm5d4RHVix+wi/bzlo1j0eeBf89Q9oM7TswWc/C7f9DPcth0c2wp1zoGkiTLkFFrxhpj/ocXXJ8X1uAlsYCftmQ+JFcOsME1JY8qHnGrDhR1OhVJcwiMMOn19iEqIDx8L9K+D2n+Hsv5kHKcDO02jGVnuOEQFLyb9Nrt2BI8AlDO2SCIuMYX9Wvnm/w+NhwZsmhJN/pOb3yd5vQnztzzav3V6B0wmHtkJMZ+hwjvHyMrd7pGmnnG/vhB/uMW06nVjwpvnOdbsc7NmeDb+tmQw5+2Hfmpqfs3XOKRGOAC9euzmQWup1GjCo3DGdAJRSfwJW4AWt9c/lL6SUGguMBYiLiyM5OblOBuXk5NT5XG8S79TEhCiem7qCyzrYaBSkiA5WNAmuTKf3un4gqN3D9F35OEF/vkV+cCxLtufBjuTjR0Z2e4a8/EKKYvrBLgfdYgbTePGHLGYgxQEhJ21715QpxAI5Cz9hub3n8UXda0L0oaX0yDvE2h5/53Bwf1iXyvGvjHYyNCCCQwsns/lwbIXnh+SlE5mVwv5m552wzxufdaddW4nBxkLXdZ1aU1DkJLfYSlNgkzURS0EW2444SV7wJ83jL6Hjto/g84sBOBbRgVV9XkdbrFXep8nh1fQCNlo604Wf2fLnT6TvchCcn8FgRz6bD8MRHc5gYOuscextcZFX2+1xtJPhKdOwOgvZk5nLjva3ndTlatNm5SwicdM7pCeMJKtxtzL7Io+m0Hv1KxyIPZPNUdcz1DKL/fM+YuteDzwmtWbI+p8IArYumcXeGlwz5uBiuqf8i/zgONb0+gcFIc3K7PfoZ6219soPcBXwcanXNwHvlTtmOvA9YAPaYp4Cjau6br9+/XRdmTdvXp3P9TY/rt6r2zw1Xbd+suTn9k+X6lV7jlR/ctoKrV9qpvXvr1e4u0y79yzV+vlGWi8ed/JGFxdr/VpbrV9pYa6ZvqZ250+51ZzvsFe8f/KNWr/RVWuns+L9P95n7ns07YRdXvmsv7ld67d7H3+ZXVCkWz85Xae+fYHW/4jSOjdTvzd3q2795HSdle9qU3aG1lt/1fqHe4yt+9dXf59F/zXHHtuv9csJWs943Gzf8ovZvmuhef1WL62/vqbMqafzd/w4h3eadrzd2/xeMv6kLlerNu9cYO75amutM3eUbD+4Vev/62Te0/wss23yjWZbcfFJ2ae11jp9tbnv8420nv5I9cfnZmr9eget3+2v9atttP6/jid8d2r7WQPLdSXPVW+GhvYCLUu9boG7G1tCGjBNa12ktd4JbAE6etGm05ZLeyWw5Jlz+Om+YXx66wAePrcTK/Yc4bL3/+SWCUvJzKkiVtm8Lzy2xcTWq6PlAGg52ExrUXySyypmrIO8TBjxjBm7sGZyzc8tzC6b06iIdmfBsbQTB1W5ca/tsPP32tldV8qtRZBXaN6/ffEjYPA9EBpF14RGAGxIP2YOCo+FDufCkPvN631rq7/PwY0Q0sScG9PRDBCEkiqqmE7md4dzTejCPXFhQRbKWXRSTawzmdtrHuY54Ap1jX4fOl0Is544ddVtOxcAyuRhJl1nvocHt8BnF5mw6bUTIdh8hiRebEI56StP/r5bfjH3bdwaMrdVf/zPT5v80JWfwG2zQFnh01GQuuzkbakAbwrBMqCjUqqtUioQuBaYVu6YH4AkAKVUDCZUVMl/fcMnNiKYHi0iGZEYy4PnduSPJ8/mqQsTWbQjk7//uL7qk4PCax6WOeN+U1q66aeTM9gdgup2OXS6wCSjayoum2aAI79sTqM8bZPK3qc0uYdKSikr2u8m77CplPLEKGt7TpkxBLl2UxGU1vEGOP9FALq5hCDFLQRuYjqa5H5N4sMHN0PTLubzbJpYUkJ6aDOERkNYtHnd4RwoyoV5L8OXV8BrbUnc9PbJtbEuHNkF7/WHtf+r2fEHN5rfsV3hqk8gvid89xdTPu1tdi2AZj3hms/N92fyDUYEtNPk0OJK1bN0Ot88gDdNP/n7bvnZdNhaDqo+r7NlNqydDMMeMbbGJprcWWiUKSP3Al4TAq21A7gPmA1sBKZorVOUUv9USl3qOmw2kKmU2gDMAx7XWmd6y6b6RnhQAHef1Z6Hzu3IzHX7mblun2cu3PlCiGoPc16Awzvrfp3t88wDKyIeel1rKi1q+kVdOwUatzL/GJUR3R4atai4x+/2Bpq0MUJQ2dw7c1+EuS+ZUsCq5ufZv/5EscjcbnqNn11sKqMO7yjjEeS6PILQwJJ4b2xEME0jgkhJL3ctixXiu8P+ajwCrU3ZcNPO5nVMJ8hON7a5E8Vu2gw3lUV/vmX2tRpC3IEFkJFS9T08zZ7F5kFa0cj4iji4GSISIKSxeT+v/hR0sSmbrs6rKMqHxeNMtVxxLb2fonxIW2bet3ZJMPJf5rulLEYEYhPLHh/SxBRsbJpZu/uUJ+egqSrrNBKiO0BWKtjzSvZn7zfl4m92M9V9395lRPLMx0uOadIaxiaX3eZBvDqOQGs9U2vdSWvdXmv9smvbc1rraa6/tdb6Ea11V611D611LWIL/sPY4e3o0TyS535cz+FcD6xfYLGamU8LsuDjc+vmbhYVwJ5F5h8KoOP55h9nzaTqz81xCUaPq6v2YpQy4aGdC058QOxZBNYg493kZJiHZ3kObYUVnxt3POU7WP11xfdJXQrjhppxGcmvGi9i8Tj471DY/ad5gKyeCMf2QmTz46fluTyCsMCyib9uCY1KQkOladbLhIaqetjlHICCo8YTgJLfh7a6PIVOJccGhcNNP8Dtv8BDa2HMlzisIfD7a5VfvyZsnWPaXlMvKs31/dn9Z80mwzuwsexDN6odjHzV9NYXvVfxOY5C85m83Rt+fhJWfFb7KVRSl0Cx3ZTegqlUu2oC3DG77PtamsSLjSd2aGvt7lWabb8C2vyPxHQw20qHO3fON15V877Gc2w73ISEAgLLXic4slbFGLVBRhbXAwKsFl6/qidH84r4x08p7DqUy4+r9/LWnC3szqzhHETlaTUI7pgDQRGmqmXuSyYuOeVmU9q34/eq/6lTl5hy1XZJLiODoNsVJuSTW41Tt/4704OsKizkpu1ZJlaasa7s9t0LoXk/6HiBeV1ReGjOC2ALgTt+Nb3AmU/AoXLxWWcxzHjEDNJqPQyS/wX/7mgeNm2Hwz1L4K7f4Ok0eDgFLnz9+Kl5dpdHEFS2CqhbQiO2HsihoKjcYLL4nqYk8UgVXpi7VNT9oHR7Brv/NO9DaY8ATI+11SDzgAiNIq3FJaakN2NDyTHFRUa4a8qaiZCx3oT6aoJbCI7tNSHHqnA6TUimaZey2/vcaB66c1+E/eU+66J8M9XKz0+aHvUt040n+ftrlY/+zUozMfXSn/euP0yop9UQ81op6H6l8Soro/Mo83vOC/DDvfDhWWYwZ1XjZo6mwsovSsYgbJltSomb9TL2Q9k8wd4VYAuFqz6FMV/BtV+XDVGdAkQI6gldmjXi3hEd+HF1Okn/TubByat5a85Wrv9oCQey6zgHfkwHuPM3SOhj4ugrPje9tW2/mekt3h9kBqAteMP0lBePK3mg7JgHloCyYx36uOZD+k9XE3td+82JK7A5Ck3PPK6HGUVdHe7e245S4SF7rom1tx4CjVuaf65yQtAoa6OJ7Q59ECLi4Irxpof17e1lH4rLJ5gHzwUvw3UT4S8LoO8tcNk4uH4KNHKV7CkFkS2M4Lmo3COIpNip2ZJRbi6gZr3M76rCQ24hcHsCjVsbz2eDK71WWc/VRVqLS00ew+0V7PoT3ukLH55pBv9Vh7PYhPzAPMyqw55nQlGdLjSvq5t19ehuKMo7MQyjFFzyjvEqJ44pscFRCP+7yXiFoz+A22YYgT7necjeZ5Z8rYjVE414Jr9Ssm3nAkjoXZIMrgmNW0KLgea7tHW26Tht/MkIQvqqE493OuHbO2Da/TBumPlebp/ryjcoE5KFE4WgWW8zCNRHiBDUI+4d0YHHzu/Ev67owYwHhvHdPWdwONfO7Z8tOx6vrjVh0aYq4alUeGYv3LfMDFa7bJyJ3ya/YtY2SP6X6ZFNON+4sTuSocUA84/hpnk/0/vue4v5cn93J3x0dknvNDvDDCDbvxaG3FMz+xo1M73g0vHntGUmptzqDPO6XZLp7blFR2vab/8MwuPMiGyARgmmSmXfmhKbcg+ZHmjbM403AyY5d/Gb0Pu6at3wkhxBWY+ge0IkUEHCOLaLEc+qEsYHN5kQQHiceW0NMEK3d7l5HVO1EDhsETD4bjP54I/3mkSowsxaOvX26pP56auN59FqiLEzfXXVx+9bY6pt+txoHuK7/qz6+ONCV0EnICzaiK8tFL68zOR1pt5uQiuXvGU6Gm7aDDVVU3+8idVRzivWGtZNNbH/9d+ZkJo913wn2wyv2r6KuOl7eGwrPL4Nbp1uErfaCZ9ccGJ4as0k4y0PHGs85i9GQ+GxEs81KNzkR9xC4LCbcGGLfrW3y4OIENQjAgMs3Hd2R64b2IpuCZH0bdWE967vw4b0Y9w3cSWO4jqO0lTK9JLcDz5bsHkQjp0Hz+yDZw/Ac0fg2klweJfpXaavLgkLlablABj1Ojy8wbi5OfthfJIRk49GmN731Z9B7+trbl/X0cYDcSeI9ywGlLkXGDuKckselks+JPLYJkh6ukxyl8SL4IapkHvQ2PT11eYBMerfdYq9uj2C8kLQMiqEiOCAExPGAUFGDKoqIS1dMeTGHR6yhZrkeXUMvsd4Bau+gn63wl8XwUVvmCkpfn2u6nO3zQGUEc2A4Oq9Avd73nIgtHblVKrCncupzLNJ6A13L4Ah95k8wKbpMPI1047ynPMc5B+hZWq5KbkzUkxcP+lp8579/rr5zjiLjDdRW4LCTSmvm+b94C/zjUf6470lXkn+EfP+thhobL5nCQx9yISx2iWVnB/dvkQIMtZDcaG5pg/xnS8ieIRzusTx4mXd+dv363n2h/X864oeqFIPEUex0z14r24Ehpb8nTgK/vI7fHOL6Ql2OLfy8ywW6HKJGbPw0wMmvBTZEm6fbXrdtWHogyacNOMxUzmxe6GpwAk2PW/aDDe9vx3JkLYcfv07h6IHEtPnphOv1fE8+OtCmHafKek74/6SB20tyXXlCMKCyv4bKaXo2qwR6/dWkDCO72Xuq/WJ4uOuGOpySdntxyuIOpaZ3qJSQqPgukmm19ruLLOt363GC1r8vkv4I01PtUlbGHBHybnb5pikZXR7I8DrvoHzXyr7PShN2jJT/RUeC63PMA/uY+nGAwPT4y2d9Dy4CRo1L/nsKsIWYkJ13S434Z/y74ebZr2g2+W03DgNjv2zJIy3/luTC+h/uyn5/fMd0zu3BJjvoycIi4brvzH/CzMfK2lb/mG46HvzOQWGwnn/OPHc6A7GYwPjpYAIgXDy3DCoNfuOFvDevG3ERgTxyPnmwbE27Sj3fL0Sh72Ap5vs5eKeCVgtJ1l1ENXWVKlkpNTMnQ1vagbp7Eg2yVJ3DXxtCAo3s69+cwssGWce9qXDBCGNIaEvLPrAJGO7XUFK9A2cVVnMNbwpXDfZPMQS+tTeHhd5hcVYFAQFnPhw7pYQycSluyl26rLvebNeZuLA7H0lD0twJVE3mwdJ03Lx8+NCUAvBqqjne8ErJlHtrsyx2EwvOb6H6dHnHTY9fHeJYt+bzdiADT8aD7Ei0paXlAC3doXqdi+EHldB1l7jBQ64E856wuw7sPHE9lVGi/7VH3POc7BxBvz8lBkboLURgnZJEBYDZzxgeuybXAnmUjPHnjQBgXD156XEQJmQUHUdnegOxnvIO2yEICzWdJJ8iISGGgiPnt+JMf1b8s7cbXy5aBeTl+7hqv8uQmsIUPDg5NWMfGs+f247VO21qsUWXLuYplLQfkTdRMBN19FmErY5z5swkLvyw027JCMCvW+EKz9GW6rp4yhlHn6VjWquAbl2B2GBAWU8MDfdEhpRUORkx8GcsjvcDwl3nmDvSpNUfKUZfODqrcb3KHuOWwCqSRRXizXAxOCf2AnPHoQnd5lcxC/PmgfojmTjRbg9vdZDTXJz5ecVX+9YuqkUauEK0cX3NCGpXX8YYfvxHlPa++fb5sHnLDYVQzUpEqgpUe3Y0+pq08Pe+qt5sB7dbYQIjBi4PZ665Aeqwy0GiRebh/mIGiw5G+OaPOHQVmNv835eKwutKSIEDQSlFC9f3p1zu8Ty9x9TeOq7dQxqF8VP9w/jn0NDeO/6Pjicmru+WM62AznVX/B0Qym48P9MCAhOFIJhD8E1X5i1G6qZ1M1T5NuLTygdddOteSUjjOO6A8rkCbIzTHVV3mHTa77oTTMNeZthZc9pmghnPgE9x5y80a4yUwICTe846WmT3Nz4k8khBEca78p9bN+bzZiN8ktmgvEGoEQILFZoNdh4BMs+MsIy8C8mPLP0Y1Nk4CiouUdQQ/a0utwk0Wc8ambBtQaafJCboQ9BuxE1K1euCwGBpuTzgZXGO60Odwlp+kojjD4OC4EIQYMiwGrh3ev6cnHPZjx8bic+u20gUWGBWJTi4p4JTLprMME2K/dNXHlijXt9IKaDCQV0GlkSD3YTFGG8hprE0D1Err34hNJRN+2bhhMYYDkxYRwUbh4EacvMmI38I3D9/0xMfMAdphqmfO/QYjFTczdu5flG9LnJeBxzXjBlw+1GlC1jHHiXCWd9c9uJ1U5py0x4qbQH02aoCXH9+pwZQHXha6ZiZsl/S8otPekRANpiMyJ6dLdJMHc8v2wOIiwGbv7hxJJVT1NT77JxK5OvWDvFvPZxxRCIEDQ4QgKtvHd9Xx48t+MJ+YD4yGDevKYXm/Zn88/pJQOO8uyO00IYpq9NN2syVMUZ95sH52lAXqGjUo/AZrWQGB9xokcA5sG67Vez7vBl758YCjqVWAPgvH+a8tLsfScWAASGmXCSu74/K61k394VJtRlCy7Z1to1rsQWYrwzpWD4I2Zywt/+afbVMTlfJW2HQy9XJVr3Kz1/fU9itZlBbO7J7E4iT+UpJFnsZyR1juXus9oz7vft2B1Odh7KZU3qUUIDrbx7fV/O6tTUJ3blFjp4YupaGofYWPDk2Sef1D4F5NodhNoq/xfqlhDJjLXpFSeM1081IYvT4aHV6QITP9+1wExkV56IeLhhCkwYCV9dZTyXqHamh1++Miuhj+mRD7jTnAcmXNTqDNiz0MTRS4898SQjXzGrtyVe7J3re5LojqaENLqDEVkfIx6BH/Lo+Z0Y2CaK71ftxak1dw5vR0LjEG77dCkf/r795MpN68is9fvJsxeTnlXA/K2n31J+FZFXRY4AYHC7KI4VOFidWm5Eb58b4ZK3TZjrdEApM/fUVRPKVjKVJq6bycHkZJgKma+uMCOEWw4se5zVBjd8Y8SlNMMfNb89nB8oQ0gTOOO+E+foOR2Jdo0wbl6DyqhTgHgEfojNauHruwZRVOw8PnPmA+d04PFv1vKvWZvYuO8Y/766FwHWU9dPmLoildbRoeQUOPjf0lRGdK54VbLTidxCBy2bVFJfDyR1isVqUfy2MYN+rUv1+kKjKh4g5UsatzQ/VdF+BDyxw8yWmbnVTJDXtfzqs5XQ4Rzj/VQ19sSfcCeMT4NEMYgQ+C02qwVbqQd9aGAA713fh8S5Ebzx6xY08OY1vU9JiCb1cB6LdxzmsfM7cazAwYQ/dnIwu5CmEUHVn+xD8uzFJ4wqLk1kqI0BbZrw28YDPDHSy4nKU4VSJlFfPllfk/OumuAdm+ojLQeZUtt2Sb62BJDQkFAKpRT3n9ORJ0Z25sfV6Tz93VqcTu+HiaauSEMpuLxvC67p3xKHU/PdyrTqT/QxefbiE0YVl+fcLnFszsgm9XBelccJfkZcV3gm7eTHhngIEQLhBO5J6sAD53RkyvI0Xvgpxas5A6dT8+3KNIa2j6F54xA6xIYzoE0T/rcs1Se5itqQZ3dU6RGAmQIE4LeNGafCJEGoEyIEQoU8fG5Hxp7Zji8W7Wbc795bPXTxzkzSjuRzdf+SydTGDGjFjkO5LNt1xGv3PVnsDidFxbpaIWgbE0a7pmH8tunAKbJMEGqPCIFQIUopnr4wkUt7JfDaz5uYsbbiZTLz7cXMTtlPUQ1nPnU6NT+v38cnf+zk4wU7eOe3rUQEBXB+1/jjx4zqEU9EUADvz9t2WoxvqIjji9JUMqCsNOd2iWPxjkyyC3y0sLwgVIMIgVApSilev6on/Vs34eEpq1mxu2wPffP+bEa//wd/+XIF45KrWZDbdfyY8Yu4+6uVvDh9Ay/N2MjiHYe5qn8LQkr1rEMDA3jw3I78vuUgl73/J9vLz9dzGuBeuD6sivJRN+ckxlJUrFmw1QPzPAmCF5CqIaFKgm1Wxt/cn8s/+JNbJyzlrM5NGdg2iqJizes/byIi2MbANlG8O28bF/dKoG2Mmf+/0FHMJ3/sJDPHjkVBVn4R363cS0RwAK9f1ZMLusaDMsUkERUkXO8c3o72TcN5ZMpqLnn3D167sieX9Kqkxt0H5FWwcH1l9GvdhMgQG3M2ZjCqRy2rbQThFOBVIVBKjQTeBqzAx1rrVys57kpgKjBAa73cmzYJtScqLJAvbh/IG79sYcnOTKa7wkRndmrKG1f3QmvNOW/8zrM/rOOrOwZhL3by169WMnfTAcICrbhTvpf3ac7To7oQFVazAT8jEmOZ+eBw7v16JY9OWUOP5pG0iQmr/sRTQF4tPIIAq4URnZsyb9MBCoqKCbadmknxBKGmeE0IlFJW4H3gPCANWKaUmqa13lDuuAjgQWCJt2wRTp7W0WG8c10ftNakHs4nPSufgW2isLjGGTxxYSJ//2E9U5anMjslg7mbDvDSZd25cXDrk7pvs8gQ/ntjP87+dzIvTt/AJ7cO8ERzTprcWuQIAK7p35IfVqfz0fwd3H9OR2+aJgi1xps5goHANq31Dq21HZgMVDQM8UXgNaCOK7ALpxKlFK2iQxncLvq4CADcMLAVvVs25slv1zF30wFevvzkRcBNXKNgHjy3I79tOlCmDHNfVj6zU/ZzNM/ukfvUhrzCiheur4wzOsRwUY9mvDdvm4wpEE47lLdqtZVSVwEjtdZ3ul7fBAzSWt9X6pi+wN+01lcqpZKBxyoKDSmlxgJjAeLi4vpNnjy5Tjbl5OQQHu7BFYrqCaeq3anZTv6zooBL29tIaln3BV8qwuHU/P3PfBxOeHlYCEv2Ofh6o52CYrAoSIyyMKRZAMOam4VivN3mxekOxq0t5JVhISSE16w/dbjAydML8ukabeXBvsHVn1AH/PE77o9thtq3e8SIESu01hVObuSzZLFSygK8Cdxa3bFa6/HAeID+/fvrpKSkOt0zOTmZup5bnzmV7b7xYl3hil2eILjlIW78ZAkvrYA9h+0MahvFPSM6sGRHJj+n7OeT9bl06NSZ6wa28nqb9y3dA2vXMWL4EJpFhtT4vAMh23l11iac8V04OzGu0uOy8ot4/edNjOrRjKEdYio8xu5wsv1gDm2iw45XXfnjd9wf2wyebbc3hWAvUHoWqxaubW4igO5AsuvBEQ9MU0pdKgnj+ou3RABgWMcYLu7ZjF82ZPDsRV24fWhbLBbFWZ2a8tj5nbnl06W8MC2F3i1rsErUSZJbi6qh0tw+tC1TV6Tx/LQU+reJolFwxZ7TC9NS+H7VXr5esoer+rXgb6O6EGSzsGh7Jgu2HmLVniNs3JeNvdjJ8I4xfH7bwDKhOkGoDd4UgmVAR6VUW4wAXAtc796ptc4Cjnd1qgoNCYKb/4zpTW6hg8ahZSuPLBbFf8b0ZtTbC7h34kqe6OXd6SncVUPVjSwuT2CAhVcu78ENHy/m9k+X8cUdA08Qk5/X7+P7VXv5a1J7FDB+/g5mp+ynsMiJvdhJsM1CrxaNuXVoG4qdmk/+2MmEP3dy5/B2nmqe4Gd4TQi01g6l1H3AbEz56AStdYpS6p/Acq31NG/dW2i42KyWE0TATUx4EG9f24cbPl7Mf9dYWVG4gZT0Y2QcK+DmIW24YVArj02tnWcvJjCg7AyuNWVg2yjevrYP901cydgvVvDxLf2Pl5QezC7kme/X06N5JI+c1wmb1cIlvRL4IHk78Y2COKtTLP3bNDl+vNaa3Zl5vP7zZoa0j/ZI2wT/w6s5Aq31TGBmuW0VrsahtU7ypi2CfzCkfTQPn9uJN37dwsYju0mMj6BRiI3np6Uwcckenh6VSKMQG/uzCsjMtTO6d0Kl4ZmqyLM7CKulN1CaUT2a8fpVvXjsmzWM/XIFl/ZKICrMxleL95BT6ODNa3odF5kuzRrx7nUVL2foHv098q35PDh5NY972RMSGiYyslhocNx3dgfiC1O5/IIkAqwWtNbMTtnPSzM2cuuny8ocu2r3Ed4c07vW98gtLK51fqA8V/VrQX5RMS9MS2F+qbWa/zaqCx3jar6cY1RYIG9c04ubPlnKZylWzhuhJV8g1AoRAqHBoZSiaajleBhIKcXI7s1I6hzLrxsyCAuyEt8ohGlr0hn3+3ZG92le67WaazIFdU24aXBrrujTnMwcO4dd4yF6tYis9XWGd2zKo+cZT+if0zfw/CVdvZq4FxoWIgSC3xBss5aZr6h9bBi/btjPM9+t45eHz6x2kZnS5NqLCa3F8VURFhRAWFAAraIrX/ayJtx3dgdStu7gs4W7CA8K4LELOnvEPqHhI7OPCn5LUICVV6/syd6j+bzxy5Yan7c27Sir9xyhafjptUi6UooxnQO5bmBL3pu3jQcmrWLS0j2s35uFo4bThAv+iXgEgl8zoE0UNw5uxacLd7I7M5cCRzGFRU7O6RLHHcPaEhhQtq+0cPsh7vp8OU3CAnn2oq4+srpylFK8dFkPAq0Wvl+1l2lr0gHoltCIiXcNJjLEsyO+hYaBbLpoEQAAEBJJREFUeASC3/PkyESSOjVlX1YBBUVOioqdvPbzJi56ZwFLdmRSUFTMhvRjfLFoF7d+uozmTUKYevcZp81MqOWxWhT/GN2dNc+fz++PJ/Hy5d3ZkpHNXZ8vP20X+hF8i3gEgt8TEWzj09sGltn228YMnvsxhTHjF2NR4HRVZfZp1ZgJtwygSQ2n0vYlSilaR4fROjqMyBAb909axX0TVzHuxr4eG08hNAxECAShAs7pEscZ7WP4fNEu8uzFdIwNp2NcOB1jI7DWw9LMi3smcCTXzt9/TOG6jxbTLSGSuEbBdGkWwVmdmnqswkhrzeRlqSR1blqrOZgE3yJCIAiVEBJo5e6z2vvaDI9x05A2FDqcTFy6h29XpJHtmi9pQJsm/O2irh6Zo2n57iM8/d06RvdO4O1rKx4Ed7JsP5jDbxszuHNYOxkv4SFECATBj7hzeLvjcxLlFjr4cXU6b/66mcve/5Mr+jbnX1f0ICig7uMjvli0G4AZa/fx9IVdiI/07HTbdoeTe75ayeaMbNo3DeecLpXP4CrUHAkUCoKfEhYUwPWDWpH8+AjuSWrPdyv3cu/XK7E7SkpNtdY1Lj09cKyAWev2cUG3OJxa88WiXR63+cPft7M5I5vwoADG/b7d49f3V0QIBMHPCQ8K4ImRibx4WXfmbDzAA5NWUVTsZPmuw1w7fjEdn53FS9M3kG+vuuJo0tJUHE7NUxd24byucUxcuqfac2rDtgM5vDt3Gxf1aMaj53di2a4jLN912GPXP5UUOoqZsXbfaVPFJUIgCAJgprt47uKu/Jyyn7PfSOaqcYvYcSiXC7vH8/EfO7nw7fks3HaowodXUbGTiUt3c2anprSNCeP2oW05mlfEd6vSPGKb06l55rt1BNssPH9pV8YMaEmTUFu99QrenrOVeyeu5LqPFnMop9DX5kiOQBCEEm4f1hanNmscPHVhIrcMaUNIoJWF2w/x1LfruP7jJQAE2yw0jQjixkGtueWMNszddICMY4W8fJlZp3pg2yi6N2/EhD92cnW/lszZmMH/lqVisyoGtIliQNsonE7N6tSjrNubRae4CO4+q32FFVlaa96as4Wluw7z+pU9iY0weYebh7Th7d+2MiKqflUnbd6fzfj5O+jfugnr07O4/IM/+fTWAWTlO/hmeSpzNh7gtSt7nNL8hwiBIAhlKJ1QdnNG+xh+fmg409fu42B2IUfz7Gzcl82/Zm3ii0W7CQm00rxxCCMSYwEzhuH2oW15ZMoaBr4yh6N5RTRvHEJggIU5Gw+UuXZMeCA/rk5n1Z6jvHVtb8JLzeGUlV/EY9+s4dcNGYzuncDV/Vsc33fLGW34cP52Zu4s4gYvvh8nw5wNGbz12xaev6QbA9oY8Xvm+3VEBAcw/ub+7Dmcx52fL+f8/8zHqc1CR8E2K6/9vIkRnWNPWVWUCIEgCDUiNDCAa/q3LLNt4bZDvDxzIynpx3hyZGKZHv3FPRP4fOEuGoXYuGVIG0YkxmK1KP6/vXsPrqI84zj+/Z2TGyRACJKUJHIzQEUExBRQEUGRAVSkLb1YL51qS6dKK5VetK11Wttpazu12lrFqq2oY1SKhTpWrUGhTAWBgsjNEm4lVG5DAAE1CXn6xy40hESSkMPRs89nJpOz7+7ZfZ+8yT7Zd8++78533mPZ5irS4jEGFXciv2MWM1/bzI/+uobJ9/+TO644i0PVtezY/z4zFmxgW9W73H55f66/oOcxzzvkZWfw+U90Z+Zrm7nxiWVMGlzEqH75xw0L0lz3vVJBaY/ODOvdNhP87DtUw62zV7L7QDVXPbiI2yacSVZ6jGVbqvjVZwaRl51BXnYGf7npfGbM38iAoo5cNrCQ8rU7uLlsBS+s3s6Es7u1SV1OxBOBc67Vzi85jb9OHcHyrVUMKj72OYSMtBhzpo447j35HbIY3+AEd915PenZJftov/kR3TplUTZlOKU98xo9/jcv7UtlZSWLN+7h+Te306ldOuMHfIyJgwoZ1rtLsx/+W7p5D7988S36FXTghWkXtskDdj/721qqDtXw5FeG8/DCTdz53BpiguG98/j0kKKj2xV3bs+dkwYcXb58YCH3lq/n3vL1jDvrY6fkqsATgXPupMRi4twejZ+oW2Jk3668OG0kq/+7n64dMunaIZP8DpkfOB1op3bpXNM/k/svHMnCit3MCQfaK1uylYKOmXxrbD8mn1t8whP7vfMqiAne2vEOC9bvbvH8FA29vmkPZUu2MmVkb847owvDeuXxwIINPL1kKz/95NkfWJ94THz94j5Me2oFL63ZzrgB3dhzsJpf/G0dY88qSMi9A08EzrkPjcLcdhTmtvzmb3o8xuh++Yzul8+71YcpX7eDRxZu4tuzVjJrWSU//eTZlOTnUF1bR21d3TGzyy3/TxUL/r2L6Zf25fHFW/jDgo0nlQjerz3MbbNXUpTbjmlj+gBBsrxxVAk3jipp1j6uGBRcFdxTXsGegzXc9eI6DrxXS0l+Dpec2eqqNckTgXMupbTLiHP5wEImDOjGU0u38rPn1zL27vnEJGrD0QOvHd6DO67oT1o8xm/nVdC5fTrXj+hFPC7ueuEt1vx3P/0LO7bq+Hf/fT0bdh3kj1/6RKunM43HxNSLS7jl6Tf43rNvMqxXHndOGkDfFkxh2hIJTQSSxgH3AHHgITP7eYP1twBfBmqBXcD1ZrYlkXVyzkVDLCauGtqdMWcW8Nhrm6mpM9qnx9m2910eW7SFyqpDfPWiM5i3biffGtuX7Mw0rh7ag9/Nq+Chf2xs1VzWL63ezgPzN3DV0O6M7pd/UvWfOKiQVdv2M7C4E1cOLkzo1KMJSwSS4sB9wKVAJbBE0lwzW1Nvs+VAqZkdkvQ14C7gc4mqk3Muerp2yOSWscdO2zmwOJfb56xiwfrddMxK47rzewLQqX06ny09nccXbeHb4/q1aATVTbsPMv3pNxhY3Ik7rjj5SYvS4jF+2Ab7adaxErjvoUCFmW0EkFQGXAkcTQRm9kq97RcB1ySwPs45B8AXhnWnMDeLrz+5nJtGl9Ax6/8zt90wohczX9vM+Hv+QVpMvFdTR/e89nxqSBETBxeS3yELM+Ng9WEOvV8LgsN1xtceX0Y8Ln5/9RCy0ls/cF8yyMwSs2NpMjDOzL4cLl8LDDOzqU1s/ztgu5n9pJF1U4ApAAUFBeeWlZW1qk4HDhwgJyenVe/9KIti3FGMGaIZ98nEfLjOGv2I6ctbati0r46MOKTFYMPeOjbuq0NA5yzxTrVR02AsPgHTSzMZcNqpufXa0rhHjx69zMxKG1v3obhZLOkaoBS4qLH1ZvYg8CBAaWmpjRo1qlXHefXVV2ntez/Kohh3FGOGaMadiJgb21vFzgPMWbGNbXvf5bScTLpkZ5AdPgVtQN/8nDZ7GK052jLuRCaCbUD9xxCLw7JjSBoDfB+4yMySP/qSc841oiQ/h+kN7jWkikSOProE6COpl6QM4PPA3PobSDoHmAFMNLOdjezDOedcgiUsEZhZLTAVeBFYCzxtZqsl/VjSxHCzXwI5wDOSVkia28TunHPOJUhC7xGY2fPA8w3Kfljv9ZhEHt8559yJ+cQ0zjkXcZ4InHMu4jwROOdcxHkicM65iPNE4JxzEZewISYSRdIuoLUjlJ4G7G7D6nxURDHuKMYM0Yw7ijFDy+PuYWaNTrTwkUsEJ0PS0qbG2khlUYw7ijFDNOOOYszQtnF715BzzkWcJwLnnIu4qCWCB5NdgSSJYtxRjBmiGXcUY4Y2jDtS9wicc84dL2pXBM455xrwROCccxEXmUQgaZyktyRVSLo12fVJBEmnS3pF0hpJqyXdHJbnSfq7pPXh987JrmsiSIpLWi7puXC5l6TFYZs/Fc6LkTIk5UqaJWmdpLWSzotCW0v6Zvj7vUrSk5KyUrGtJT0iaaekVfXKGm1fBe4N418paUhLjhWJRCApDtwHjAf6A1dJ6p/cWiVELTDdzPoDw4GbwjhvBcrNrA9QHi6nopsJ5r444hfA3WZWAlQBNySlVolzD/CCmX0cGEQQe0q3taQi4BtAqZkNAOIEk16lYlv/CRjXoKyp9h0P9Am/pgD3t+RAkUgEwFCgwsw2mlk1UAZcmeQ6tTkze9vM/hW+fofgxFBEEOuj4WaPApOSU8PEkVQMXAY8FC4LuBiYFW6SUnFL6gSMBB4GMLNqM9tLBNqaYB6VdpLSgPbA26RgW5vZAmBPg+Km2vdKYKYFFgG5kro191hRSQRFwNZ6y5VhWcqS1BM4B1gMFJjZ2+Gq7UBBkqqVSL8BvgPUhctdgL3hTHmQem3eC9gF/DHsDntIUjYp3tZmtg34FfAfggSwD1hGard1fU2170md46KSCCJFUg7wZ2Came2vv86Czwun1GeGJV0O7DSzZcmuyymUBgwB7jezc4CDNOgGStG27kzw328voBDI5vjuk0hoy/aNSiLYBpxeb7k4LEs5ktIJksATZjY7LN5x5DIx/L4zWfVLkAuAiZI2E3T7XUzQf54bdh9A6rV5JVBpZovD5VkEiSHV23oMsMnMdplZDTCboP1Tua3ra6p9T+ocF5VEsAToE36yIIPg5tLcJNepzYX94g8Da83s1/VWzQW+GL7+IjDnVNctkczsNjMrNrOeBG07z8yuBl4BJoebpVTcZrYd2CqpX1h0CbCGFG9rgi6h4ZLah7/vR+JO2bZuoKn2nQtcF356aDiwr14X0omZWSS+gAnAv4ENwPeTXZ8ExTiC4FJxJbAi/JpA0F9eDqwHXgbykl3XBP4MRgHPha97A68DFcAzQGay69fGsQ4Globt/RegcxTaGvgRsA5YBTwGZKZiWwNPEtwHqSG4AryhqfYFRPDJyA3AmwSfqmr2sXyICeeci7iodA0555xrgicC55yLOE8EzjkXcZ4InHMu4jwROOdcxHkicO4UkjTqyOiozn1YeCJwzrmI80TgXCMkXSPpdUkrJM0I5zo4IOnucCz8ckldw20HS1oUjgP/bL0x4kskvSzpDUn/knRGuPucevMIPBE+Ietc0ngicK4BSWcCnwMuMLPBwGHgaoIBzpaa2VnAfOCO8C0zge+a2UCCpzqPlD8B3Gdmg4DzCZ4ShWBU2GkEc2P0Jhgrx7mkSTvxJs5FziXAucCS8J/1dgSDe9UBT4XbPA7MDucFyDWz+WH5o8AzkjoARWb2LICZvQcQ7u91M6sMl1cAPYGFiQ/LucZ5InDueAIeNbPbjimUbm+wXWvHZ3m/3uvD+N+hSzLvGnLueOXAZEn5cHSe2B4Efy9HRrj8ArDQzPYBVZIuDMuvBeZbMENcpaRJ4T4yJbU/pVE410z+n4hzDZjZGkk/AF6SFCMY/fEmgslfhobrdhLcR4BgOOAHwhP9RuBLYfm1wAxJPw738ZlTGIZzzeajjzrXTJIOmFlOsuvhXFvzriHnnIs4vyJwzrmI8ysC55yLOE8EzjkXcZ4InHMu4jwROOdcxHkicM65iPsfpsfc0f53BkoAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YZpQ71BGFg5e","executionInfo":{"status":"ok","timestamp":1619610219863,"user_tz":-330,"elapsed":387977,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"20f910a0-dbe4-4303-a073-9f47a107c89b"},"source":["#check output of model\n","y_predict=model.predict(X_test)\n","\n","# define vector\n","probs = asarray(y_predict)\n","print(probs.shape)\n","# get argmax\n","result = argmax(probs, axis=1)\n","print(result)\n","\n","y_test_cm=argmax(y_test,axis=1)\n","print(result.shape)\n","\n","\n","\n","confusion = confusion_matrix(y_test_cm, result)\n","print('Confusion Matrix\\n')\n","print(confusion)\n","\n","#importing accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","print('\\nAccuracy: {:.2f}\\n'.format(accuracy_score(y_test_cm, result)))\n","\n","print('Micro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='micro')))\n","print('Micro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='micro')))\n","print('Micro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='micro')))\n","\n","print('Macro Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='macro')))\n","print('Macro Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='macro')))\n","print('Macro F1-score: {:.2f}\\n'.format(f1_score(y_test_cm, result, average='macro')))\n","\n","print('Weighted Precision: {:.2f}'.format(precision_score(y_test_cm, result, average='weighted')))\n","print('Weighted Recall: {:.2f}'.format(recall_score(y_test_cm, result, average='weighted')))\n","print('Weighted F1-score: {:.2f}'.format(f1_score(y_test_cm, result, average='weighted')))\n","\n","from sklearn.metrics import classification_report\n","print('\\nClassification Report\\n')\n","print(classification_report(y_test_cm, result, target_names=['Class A','Class B','Class C','Class D', 'Class E']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(150, 5)\n","[3 1 0 4 0 4 4 2 3 4 2 0 4 0 4 2 3 2 3 1 3 3 1 3 0 1 3 4 2 0 4 1 0 1 3 4 0\n"," 2 3 3 3 1 4 3 1 1 3 1 3 3 1 4 4 1 3 2 1 1 3 0 3 4 4 2 3 0 0 2 4 2 0 1 1 3\n"," 1 3 4 1 3 0 3 4 1 2 4 4 1 1 3 4 2 4 3 1 4 0 3 4 1 2 1 4 2 4 2 1 2 3 3 4 4\n"," 1 1 1 0 0 1 0 0 2 2 2 2 3 1 4 0 2 0 1 3 4 0 4 1 2 3 0 1 3 0 2 2 4 0 2 3 0\n"," 2 1]\n","(150,)\n","Confusion Matrix\n","\n","[[21  6  0  3  0]\n"," [ 4 25  0  1  0]\n"," [ 0  3 18  9  0]\n"," [ 0  0  8 21  1]\n"," [ 0  0  0  0 30]]\n","\n","Accuracy: 0.77\n","\n","Micro Precision: 0.77\n","Micro Recall: 0.77\n","Micro F1-score: 0.77\n","\n","Macro Precision: 0.77\n","Macro Recall: 0.77\n","Macro F1-score: 0.77\n","\n","Weighted Precision: 0.77\n","Weighted Recall: 0.77\n","Weighted F1-score: 0.77\n","\n","Classification Report\n","\n","              precision    recall  f1-score   support\n","\n","     Class A       0.84      0.70      0.76        30\n","     Class B       0.74      0.83      0.78        30\n","     Class C       0.69      0.60      0.64        30\n","     Class D       0.62      0.70      0.66        30\n","     Class E       0.97      1.00      0.98        30\n","\n","    accuracy                           0.77       150\n","   macro avg       0.77      0.77      0.77       150\n","weighted avg       0.77      0.77      0.77       150\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w7gR0TYHFoeZ","executionInfo":{"status":"ok","timestamp":1619610219866,"user_tz":-330,"elapsed":387972,"user":{"displayName":"harsh yadav","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Git-3FpfUhFpyQua1IZWmLDYMJ489cMMsibm34wNlc=s64","userId":"14797929877470114468"}},"outputId":"b99c6893-e28c-47bc-f734-996e1d661e3f"},"source":["# serialize model to JSON\n","model_json = model.to_json()\n","with open(\"/content/drive/MyDrive/mtech_finalyr_project/5 class problem/model_5class_AvsCvsDvsBvsE.json\", \"w\") as json_file:\n","    json_file.write(model_json)\n","# serialize weights to HDF5\n","model.save_weights(\"/content/drive/MyDrive/mtech_finalyr_project/5 class problem/model_5class_AvsCvsDvsBvsE.h5\")\n","print(\"Saved model to disk\")\n"," "],"execution_count":null,"outputs":[{"output_type":"stream","text":["Saved model to disk\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8iC5zis4Tu-O"},"source":["  \"\"\"\n","# load json and create model\n","json_file = open('model.json', 'r')\n","loaded_model_json = json_file.read()\n","json_file.close()\n","loaded_model = model_from_json(loaded_model_json)\n","# load weights into new model\n","loaded_model.load_weights(\"model.h5\")\n","print(\"Loaded model from disk\")\n"," \n","# evaluate loaded model on test data\n","loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n","score = loaded_model.evaluate(X, Y, verbose=0)\n","\"\"\""],"execution_count":null,"outputs":[]}]}